Aerial imagery have long been used as auxiliary information to reduce the costs of forest inventories. Due to the high correlation between tree height and forest biophysical properties, manual photogrammetric techniques have been applied to aerial imagery for the measurement of vertical canopy structure, an important variable in forest inventories. In the past decade years, major advances have resulted in the development of photogrammetric software for the automatic generation of photogrammetric data from digital imagery. As a result, user-friendly advanced photogrammetric software are now available on the market, allowing for an increasing number of users to produce dense three-dimensional (3D) photogrammetric point clouds. The increased accessibility to advanced software in addition to the large availability of aerial imagery has led to a renaissance in the use of photogrammetry for forest inventory. The smaller costs of acquiring photogrammetric data compared to alternative 3D remote sensing data (i.e., airborne laser scanning; ALS), make their use appealing. The four studies included in this thesis addressed the use of photogrammetric data for the two main categories of forest inventories, namely: forest management inventories (FMI) and large-scale forest surveys (LSFS). For both categories, this thesis illustrated potential applications for which photogrammetric data may be advantageous over alternative 3D remote sensing data. Wall-to-wall photogrammetric data produced from imagery collected using different platforms, i.e. a manned aircraft in paper I and an unmanned aerial vehicle (UAV) in paper II, were used to model forest biophysical properties of interest in FMI. Both structural and spectral variables from photogrammetric data were used as predictor variables. Furthermore, when available, accuracy figures from ALS based inventory were used as a benchmark. The accuracy assessment revealed that photogrammetric data were able to predict forest biophysical properties with similar accuracy to ALS data. Furthermore, the first two papers highlighted some advantages related to photogrammetry, namely: 1) the possibility to use spectral information for species-specific FMI, and 2) the versatility of acquiring photogrammetric data using UAVs. Moreover, the possibility to use UAVs in forest inventories was further addressed by illustrating LSFS applications for which UAVs could be cost-efficient. As a means of reducing the costs for RS auxiliary data acquisition, UAV data were acquired as a sample (i.e. partial-coverage) over a large area. In paper III the sample of UAV data, together with a subsample of field data were used in a hybrid inferential framework to estimate growing stock volume (GSV) and assess its uncertainty. Such an approach enabled an increase in precision compared to design-based estimates using only field data. In paper IV, these data sources were augmented by a third wall-to-wall layer of Sentinel-2 multispectral data as a means of further increasing the precision of the estimator. In the latter case, the recently developed hierarchical model-based inference was adopted to enable a statistically rigorous estimation of the GSV and its uncertainty. This approach resulted in a slight increase in the precision of the hybrid estimator. Nevertheless, it allowed for a reduction of the UAV sampling intensity, hence reducing the UAV acquisition costs substantially. Overall, the thesis concluded that photogrammetric data will have an increasingly important role in forest inventories. Not only are comparable levels of accuracy achievable, but their use can be more cost-efficient than alternative 3D remotely sensed data. Even though further research in different forestry settings should confirm our findings, the applications described in this thesis were found to have potential for operational use.

@PhdThesis{Puliti2017,
Title = {Use of photogrammetric 3D data for forest inventory},
Author = {Stefano Puliti},
School = {Norwegian Universtiy of Life Sciences},
Year = {2017},
Abstract = {Aerial imagery have long been used as auxiliary information to reduce the costs of forest
inventories. Due to the high correlation between tree height and forest biophysical properties,
manual photogrammetric techniques have been applied to aerial imagery for the measurement
of vertical canopy structure, an important variable in forest inventories. In the past decade
years, major advances have resulted in the development of photogrammetric software for the
automatic generation of photogrammetric data from digital imagery. As a result, user-friendly
advanced photogrammetric software are now available on the market, allowing for an increasing
number of users to produce dense three-dimensional (3D) photogrammetric point clouds. The
increased accessibility to advanced software in addition to the large availability of aerial
imagery has led to a renaissance in the use of photogrammetry for forest inventory. The
smaller costs of acquiring photogrammetric data compared to alternative 3D remote sensing
data (i.e., airborne laser scanning; ALS), make their use appealing. The four studies included
in this thesis addressed the use of photogrammetric data for the two main categories of forest
inventories, namely: forest management inventories (FMI) and large-scale forest surveys
(LSFS). For both categories, this thesis illustrated potential applications for which
photogrammetric data may be advantageous over alternative 3D remote sensing data.
Wall-to-wall photogrammetric data produced from imagery collected using different
platforms, i.e. a manned aircraft in paper I and an unmanned aerial vehicle (UAV) in paper II,
were used to model forest biophysical properties of interest in FMI. Both structural and
spectral variables from photogrammetric data were used as predictor variables. Furthermore,
when available, accuracy figures from ALS based inventory were used as a benchmark. The
accuracy assessment revealed that photogrammetric data were able to predict forest
biophysical properties with similar accuracy to ALS data. Furthermore, the first two papers
highlighted some advantages related to photogrammetry, namely: 1) the possibility to use
spectral information for species-specific FMI, and 2) the versatility of acquiring
photogrammetric data using UAVs.
Moreover, the possibility to use UAVs in forest inventories was further addressed by
illustrating LSFS applications for which UAVs could be cost-efficient. As a means of
reducing the costs for RS auxiliary data acquisition, UAV data were acquired as a sample (i.e.
partial-coverage) over a large area. In paper III the sample of UAV data, together with a
subsample of field data were used in a hybrid inferential framework to estimate growing
stock volume (GSV) and assess its uncertainty. Such an approach enabled an increase in
precision compared to design-based estimates using only field data. In paper IV, these data
sources were augmented by a third wall-to-wall layer of Sentinel-2 multispectral data as a
means of further increasing the precision of the estimator. In the latter case, the recently
developed hierarchical model-based inference was adopted to enable a statistically rigorous
estimation of the GSV and its uncertainty. This approach resulted in a slight increase in the
precision of the hybrid estimator. Nevertheless, it allowed for a reduction of the UAV
sampling intensity, hence reducing the UAV acquisition costs substantially.
Overall, the thesis concluded that photogrammetric data will have an increasingly
important role in forest inventories. Not only are comparable levels of accuracy achievable,
but their use can be more cost-efficient than alternative 3D remotely sensed data. Even
though further research in different forestry settings should confirm our findings, the
applications described in this thesis were found to have potential for operational use.},
Owner = {hanso},
Timestamp = {2017.09.06},
Url = {http://statisk.umb.no/ina/forskning/drgrader/2017-Puliti.pdf}
}

Airborne laser scanning (ALS) has been demonstrated to be an excellent source of auxiliary information for increasing the precision of estimating stand-level attributes in forest inventories. It has also been proposed to use ALS for estimating biomass and carbon stocks under the United Nations Collaborative Program on Reduced Emissions from Deforestation and Forest Degradation in Developing Countries (UN-REDD). The benefits of REDD depend among other facts on the cost-efficiency of the carbon accounting systems, which should be economically feasible and highly accurate. Acquiring full-coverage ALS data would provide highly accurate estimates but might be too expensive for limited inventory budgets. As an alternative, the ALS data might be collected as a sample by acquiring data from a portion of the area of interest. However, in surveys involving complex multi-phase and multi-stage systematic sampling designs, the efficiency of ALS-based estimates is hampered by the ability of estimating the sampling variability correctly. It has been demonstrated recently that the precision of such complex analytical estimators may be largely underestimated. In order to make an informed decision, simulated sampling from artificial populations generated from empirical data may provide a means for assessing the cost-efficiency of various sampling strategies when analytical approaches fail. This study presents a simulation-based assessment of sampling strategies employing ALS with focus on large-area (27,400 km2) biomass estimation. Simulated sampling mimicking the two contrasting cases “wall-to-wall” and two-phase ALS-aided surveys is exemplified using Norwegian National Forest Inventory data for creating an artificial population. The main results indicated that (1) the gain in precision (10%) when using “wall-to-wall” ALS data may not be worth the very high inventory costs, (2) using variance estimators based on higher-order successive differences produced correct confidence intervals for two-phase systematic sampling, and (3) two-phase ALS-aided systematic surveys are cost-efficient solutions for large-area biomass estimation.

@Article{Ene2016,
Title = {Simulation-based assessment of sampling strategies for large-area biomass estimation using wall-to-wall and partial coverage airborne laser scanning surveys},
Author = {Ene, Liviu Theodor and Næsset, Erik and Gobakken, Terje},
Journal = {Remote Sensing of Environment},
Year = {2016},
Pages = {328-340},
Volume = {176},
Abstract = {Airborne laser scanning (ALS) has been demonstrated to be an excellent source of auxiliary information for increasing the precision of estimating stand-level attributes in forest inventories. It has also been proposed to use ALS for estimating biomass and carbon stocks under the United Nations Collaborative Program on Reduced Emissions from Deforestation and Forest Degradation in Developing Countries (UN-REDD). The benefits of REDD depend among other facts on the cost-efficiency of the carbon accounting systems, which should be economically feasible and highly accurate. Acquiring full-coverage ALS data would provide highly accurate estimates but might be too expensive for limited inventory budgets. As an alternative, the ALS data might be collected as a sample by acquiring data from a portion of the area of interest. However, in surveys involving complex multi-phase and multi-stage systematic sampling designs, the efficiency of ALS-based estimates is hampered by the ability of estimating the sampling variability correctly. It has been demonstrated recently that the precision of such complex analytical estimators may be largely underestimated. In order to make an informed decision, simulated sampling from artificial populations generated from empirical data may provide a means for assessing the cost-efficiency of various sampling strategies when analytical approaches fail. This study presents a simulation-based assessment of sampling strategies employing ALS with focus on large-area (27,400 km2) biomass estimation. Simulated sampling mimicking the two contrasting cases “wall-to-wall” and two-phase ALS-aided surveys is exemplified using Norwegian National Forest Inventory data for creating an artificial population. The main results indicated that (1) the gain in precision (10%) when using “wall-to-wall” ALS data may not be worth the very high inventory costs, (2) using variance estimators based on higher-order successive differences produced correct confidence intervals for two-phase systematic sampling, and (3) two-phase ALS-aided systematic surveys are cost-efficient solutions for large-area biomass estimation.},
Doi = {http://dx.doi.org/10.1016/j.rse.2016.01.025},
ISSN = {0034-4257},
Keywords = {Airborne laser scanning Forest inventory Variance estimation Simulated sampling},
Owner = {hanso},
Timestamp = {2016.03.01},
Type = {Journal Article},
Url = {http://www.sciencedirect.com/science/article/pii/S003442571630027X}
}

For many decades remotely sensed data have been used as a source of auxiliary information when conducting regional or national surveys of forest resources. In the past decade, airborne scanning LiDAR (Light Detection and Ranging) has emerged as a promising tool for sample surveys aimed at improving estimation of above-ground forest biomass. This technology is now employed routinely in forest management inventories of some Nordic countries, and there is eager anticipation for its application to assess changes in standing biomass in vast tropical regions of the globe in concert with the UN REDD program to limit C emissions. In the rapidly expanding literature on LiDAR-assisted biomass estimation the assessment of the uncertainty of estimation varies widely, ranging from statistically rigorous to ad hoc. In many instances, too, there appears to be no recognition of different bases of statistical inference which bear importantly on uncertainty estimation. Statistically rigorous assessment of uncertainty for four large LiDAR-assisted surveys is expounded.

@Article{Gregoire2016,
Title = {Statistical rigor in LiDAR-assisted estimation of aboveground forest biomass},
Author = {Gregoire, Timothy G. and Næsset, Erik and McRoberts, Ronald E. and Ståhl, Göran and Andersen, Hans-Erik and Gobakken, Terje and Ene, Liviu and Nelson, Ross},
Journal = {Remote Sensing of Environment},
Year = {2016},
Pages = {98-108},
Volume = {173},
Abstract = {For many decades remotely sensed data have been used as a source of auxiliary information when conducting regional or national surveys of forest resources. In the past decade, airborne scanning LiDAR (Light Detection and Ranging) has emerged as a promising tool for sample surveys aimed at improving estimation of above-ground forest biomass. This technology is now employed routinely in forest management inventories of some Nordic countries, and there is eager anticipation for its application to assess changes in standing biomass in vast tropical regions of the globe in concert with the UN REDD program to limit C emissions. In the rapidly expanding literature on LiDAR-assisted biomass estimation the assessment of the uncertainty of estimation varies widely, ranging from statistically rigorous to ad hoc. In many instances, too, there appears to be no recognition of different bases of statistical inference which bear importantly on uncertainty estimation. Statistically rigorous assessment of uncertainty for four large LiDAR-assisted surveys is expounded.},
Doi = {http://dx.doi.org/10.1016/j.rse.2015.11.012},
ISSN = {0034-4257},
Keywords = {Sampling Statistical inference Variance estimation},
Owner = {hanso},
Timestamp = {2016.03.01},
Type = {Journal Article},
Url = {http://www.sciencedirect.com/science/article/pii/S0034425715302017}
}

Invasive species can be considered a threat to biodiversity, and remote sensing has been proposed as a tool for detection and monitoring of invasive species. In this study, we test the ability to discriminate between two tree species of the same genera, using data from Landsat 8 satellite imagery, aerial images, and airborne laser scanning. Ground observations from forest stands dominated by either Norway spruce (Picea abies) or Sitka spruce (Picea sitchensis) were coupled with variables derived from each of the three sets of remote sensing data. Random forest, support vector machine, and logistic regression classification models were fit to the data, and the classification accuracy tested by performing a cross-validation. Classification accuracies were compared for different combinations of remote sensing data and classification methods. The overall classification accuracy varied from 0.53 to 0.79, with the highest accuracy obtained using logistic regression with a combination of data derived from Landsat imagery and aerial images. The corresponding kappa value was 0.58. The contribution to the classification accuracy from using airborne data in addition to Landsat imagery was not substantial in this study. The classification accuracy varied between models using data from individual Landsat images.

@Article{Hauglin2016,
Title = {Discriminating between Native Norway Spruce and Invasive Sitka Spruce—A Comparison of Multitemporal Landsat 8 Imagery, Aerial Images and Airborne Laser Scanner Data},
Author = {Hauglin, Marius and Ørka, Hans Ole},
Journal = {Remote Sensing},
Year = {2016},
Number = {5},
Pages = {363},
Volume = {8},
Abstract = {Invasive species can be considered a threat to biodiversity, and remote sensing has been proposed as a tool for detection and monitoring of invasive species. In this study, we test the ability to discriminate between two tree species of the same genera, using data from Landsat 8 satellite imagery, aerial images, and airborne laser scanning. Ground observations from forest stands dominated by either Norway spruce (Picea abies) or Sitka spruce (Picea sitchensis) were coupled with variables derived from each of the three sets of remote sensing data. Random forest, support vector machine, and logistic regression classification models were fit to the data, and the classification accuracy tested by performing a cross-validation. Classification accuracies were compared for different combinations of remote sensing data and classification methods. The overall classification accuracy varied from 0.53 to 0.79, with the highest accuracy obtained using logistic regression with a combination of data derived from Landsat imagery and aerial images. The corresponding kappa value was 0.58. The contribution to the classification accuracy from using airborne data in addition to Landsat imagery was not substantial in this study. The classification accuracy varied between models using data from individual Landsat images.},
Doi = {10.3390/rs8050363},
ISSN = {2072-4292},
Owner = {hanso},
Timestamp = {2016.09.28},
Url = {http://www.mdpi.com/2072-4292/8/5/363}
}

Quantiles and proportions in a sampling distribution of a per unit area attribute (Y) depend on the spatial support (area) of employed survey plots. This is a nuisance for managers, and policy developers; in particular when the underlying data have been collected with different spatial supports. Users of these statistics may wish to calibrate their estimates to a common scale of spatial support. The easiest way to do this is through scaling to a common plot size. We demonstrate a statistical method for upscaling. The method is illustrated in the context of a design-based forest inventory of a target attribute Y with a census of a co-located vector of auxiliary variables (X) correlated with Y. Two case studies from Norway and Switzerland confirmed significant and practically important scale effects in quantiles and proportions of above ground live tree biomass (Mg ha−1) and stem volume (m3 ha−1). Upscaling requires an estimate of the spatial autocorrelation of Y given X at the scale of the original spatial support. We present an expedient method to this end. Our method affords estimation of scaled quantiles and proportions and assures consistency of sampling distribution across scales.

@Article{Magnussen2016,
Title = {Scale effects in survey estimates of proportions and quantiles of per unit area attributes},
Author = {Magnussen, Steen and Mandallaz, Daniel and Lanz, Adrian and Ginzler, Christian and Næsset, Erik and Gobakken, Terje},
Journal = {Forest Ecology and Management},
Year = {2016},
Pages = {122-129},
Volume = {364},
Abstract = {Quantiles and proportions in a sampling distribution of a per unit area attribute (Y) depend on the spatial support (area) of employed survey plots. This is a nuisance for managers, and policy developers; in particular when the underlying data have been collected with different spatial supports. Users of these statistics may wish to calibrate their estimates to a common scale of spatial support. The easiest way to do this is through scaling to a common plot size. We demonstrate a statistical method for upscaling. The method is illustrated in the context of a design-based forest inventory of a target attribute Y with a census of a co-located vector of auxiliary variables (X) correlated with Y. Two case studies from Norway and Switzerland confirmed significant and practically important scale effects in quantiles and proportions of above ground live tree biomass (Mg ha−1) and stem volume (m3 ha−1). Upscaling requires an estimate of the spatial autocorrelation of Y given X at the scale of the original spatial support. We present an expedient method to this end. Our method affords estimation of scaled quantiles and proportions and assures consistency of sampling distribution across scales.},
Doi = {http://dx.doi.org/10.1016/j.foreco.2016.01.013},
ISSN = {0378-1127},
Keywords = {Quantiles Area proportions Spatial support Spatial autocorrelation Scaled quantiles Scaled area proportions},
Owner = {hanso},
Timestamp = {2016.03.01},
Type = {Journal Article},
Url = {http://www.sciencedirect.com/science/article/pii/S0378112716000141}
}

Field surveys are often a primary source of data for aboveground biomass (AGB) and forest area estimates — two fundamental parameters in forest resource assessments and for measurement, reporting, and verification (MRV) under the United Nations Collaborative Program on Reducing Emissions from Deforestation and Forest Degradation in Developing Countries (REDD +). However, plot-based estimates of such parameters are often not sufficiently precise for their intended purposes, and especially so in developing and tropical countries in which implementation of extensive sample surveys can be cost-prohibitive or infeasible due to inaccessibility. Remotely sensed data can improve the precision of estimates and thereby reduce the need for field samples. To guide investment decision in MRV systems, comparative analyses of the contribution of different types of remotely sensed data to improve precision of estimates are required. The aim of the current study was to quantify the contribution of data from (1) airborne laser scanning (ALS), (2) interferometric synthetic aperture radar (InSAR) derived from TanDEM-X, (3) RapidEye optical imagery, and global forest map products derived from (4) Landsat and (5) ALOS PALSAR L-band radar imagery to improve precision of AGB and forest area estimates beyond the precision that could be obtained by a pure field-based survey in miombo woodlands of Tanzania. Miombo woodlands is one among the most wide-spread vegetation types in eastern, central, and southern Africa, occupying about 9% of the entire African land area. A 365.6 km2 region in Liwale district in Tanzania served as area of interest for this study. Eighty-eight ground plots distributed on 11 clusters of eight plots each according to a probability-based single-stage cluster sampling design served as field data for regression model calibration used for mapping and estimation of AGB and forest area. Model-assisted estimators were used in the estimation. The relative efficiency (RE) of the ALS-assisted estimates of mean AGB per hectare (variance of the field-based estimate relative to the variance of the ALS-assisted estimate) was 3.6. Relative efficiency translates directly to the factor by which the sample size used for the ALS-assisted estimate would have to be multiplied to arrive at the same precision for a pure field-based estimate. RE values for InSAR and RapidEye were 2.8 and 3.3, while the global Landsat and PALSAR map products contributed only marginally to improve precision (RE = 1.3–1.4). For forest area estimation, ALS-assisted estimates showed an RE of 3.7–4.6, while InSAR, RapidEye, and global Landsat and PALSAR maps resulted in RE values of 1.0–1.3, 2.0–2.1, 1.4–1.8, and 1.7, respectively.

@Article{Naesset2016,
Title = {Mapping and estimating forest area and aboveground biomass in miombo woodlands in Tanzania using data from airborne laser scanning, TanDEM-X, RapidEye, and global forest maps: A comparison of estimated precision},
Author = {Næsset, Erik and Ørka, Hans Ole and Solberg, Svein and Bollandsås, Ole Martin and Hansen, Endre Hofstad and Mauya, Ernest and Zahabu, Eliakimu and Malimbwi, Rogers and Chamuya, Nurdin and Olsson, Håkan and Gobakken, Terje},
Journal = {Remote Sensing of Environment},
Year = {2016},
Pages = {282-300},
Volume = {175},
Abstract = {Field surveys are often a primary source of data for aboveground biomass (AGB) and forest area estimates — two fundamental parameters in forest resource assessments and for measurement, reporting, and verification (MRV) under the United Nations Collaborative Program on Reducing Emissions from Deforestation and Forest Degradation in Developing Countries (REDD +). However, plot-based estimates of such parameters are often not sufficiently precise for their intended purposes, and especially so in developing and tropical countries in which implementation of extensive sample surveys can be cost-prohibitive or infeasible due to inaccessibility. Remotely sensed data can improve the precision of estimates and thereby reduce the need for field samples. To guide investment decision in MRV systems, comparative analyses of the contribution of different types of remotely sensed data to improve precision of estimates are required. The aim of the current study was to quantify the contribution of data from (1) airborne laser scanning (ALS), (2) interferometric synthetic aperture radar (InSAR) derived from TanDEM-X, (3) RapidEye optical imagery, and global forest map products derived from (4) Landsat and (5) ALOS PALSAR L-band radar imagery to improve precision of AGB and forest area estimates beyond the precision that could be obtained by a pure field-based survey in miombo woodlands of Tanzania. Miombo woodlands is one among the most wide-spread vegetation types in eastern, central, and southern Africa, occupying about 9% of the entire African land area. A 365.6 km2 region in Liwale district in Tanzania served as area of interest for this study. Eighty-eight ground plots distributed on 11 clusters of eight plots each according to a probability-based single-stage cluster sampling design served as field data for regression model calibration used for mapping and estimation of AGB and forest area. Model-assisted estimators were used in the estimation. The relative efficiency (RE) of the ALS-assisted estimates of mean AGB per hectare (variance of the field-based estimate relative to the variance of the ALS-assisted estimate) was 3.6. Relative efficiency translates directly to the factor by which the sample size used for the ALS-assisted estimate would have to be multiplied to arrive at the same precision for a pure field-based estimate. RE values for InSAR and RapidEye were 2.8 and 3.3, while the global Landsat and PALSAR map products contributed only marginally to improve precision (RE = 1.3–1.4). For forest area estimation, ALS-assisted estimates showed an RE of 3.7–4.6, while InSAR, RapidEye, and global Landsat and PALSAR maps resulted in RE values of 1.0–1.3, 2.0–2.1, 1.4–1.8, and 1.7, respectively.},
Doi = {http://dx.doi.org/10.1016/j.rse.2016.01.006},
ISSN = {0034-4257},
Keywords = {Miombo woodlands Tropical forests REDD + Model-assisted estimation Aboveground biomass Forest area Airborne laser TanDEM-X InSAR RapidEye Global Landsat maps Global ALOS PALSAR maps},
Owner = {hanso},
Timestamp = {2016.03.01},
Type = {Journal Article},
Url = {http://www.sciencedirect.com/science/article/pii/S0034425716300062}
}

<p>The nature areas surrounding the capital of Norway (Oslomarka), comprising 1 700 km<sup>2</sup> of forest land, are the recreational home turf for a population of 1.2 mill. people. These areas are highly valuable, not only for recreational purposes and biodiversity, but also for commercial activities. To assess the impacts of the challenges that Oslo municipality forest face in their management, we developed four optimization problems with different levels of management constraints. The constraints consider control of harvest level, guarantee of minimum old-growth forest area and maximum open area after final harvest. For the latter, to date, no appropriate analyses quantifying the impact of such a constraint on economy and biomass production have been carried out in Norway. The problem solved is large due to both the number of stands and number of treatment schedules. However, the model applied demonstrated its relevance for solving large problems involving maximum opening areas. The inclusion of maximum open area constraints caused 7.0% loss in NPV compared to the business as usual case with controlled harvest volume and minimum old-growth area. The estimated supply of 20-30 GWh annual energy from harvest residues could provide a small, but stable supply of energy to the municipality.</p>

@Article{Borges2015a,
Title = {Impact of maximum opening area constraints on profitability and biomass availability in forestry – a large, real world case},
Author = {Borges, Paulo and Bergseng, Even and Eid, Tron and Gobakken, Terje},
Journal = {SILVA FENNICA},
Year = {2015},
Number = {5},
Volume = {49},
Abstract = {<p>The nature areas surrounding the capital of Norway (Oslomarka), comprising 1 700 km<sup>2</sup> of forest land, are the recreational home turf for a population of 1.2 mill. people. These areas are highly valuable, not only for recreational purposes and biodiversity, but also for commercial activities. To assess the impacts of the challenges that Oslo municipality forest face in their management, we developed four optimization problems with different levels of management constraints. The constraints consider control of harvest level, guarantee of minimum old-growth forest area and maximum open area after final harvest. For the latter, to date, no appropriate analyses quantifying the impact of such a constraint on economy and biomass production have been carried out in Norway. The problem solved is large due to both the number of stands and number of treatment schedules. However, the model applied demonstrated its relevance for solving large problems involving maximum opening areas. The inclusion of maximum open area constraints caused 7.0% loss in NPV compared to the business as usual case with controlled harvest volume and minimum old-growth area. The estimated supply of 20-30 GWh annual energy from harvest residues could provide a small, but stable supply of energy to the municipality.</p>},
Doi = {doi:10.14214/sf.1347},
Owner = {hanso},
Timestamp = {2016.03.01},
Type = {Journal Article},
Url = {http://www.silvafennica.fi/article/1347}
}

ABSTRACTGreen-up requirements are of great interest for forests near cities since these forests are commonly used for recreational activities by the local population as well as for commercial forestry activities. We present three formulations to establish green-up requirements, based on a dynamic green-up approach and constructed by means of: (i) a predefined fixed length for the green-up time, (ii) a predefined variable length for the green-up time and (iii) height information produced by the growth simulator. Additionally, restrictions on harvested volume and maximum open areas were applied. All the green-up formulations were applied to five datasets comprising different initial forest conditions regarding age and site index distribution. Results show that higher net present values are obtained by the formulation that allow a predefined variable length for the green-up time and by using the height information from the growth simulator compared to the formulations using a predefined fixed length for the green-up time. The increase in NPV was most pronounced for the old forest datasets and varied between 4.23% and 8.15%. The optimal solution was always found when modeling the green-up requirement using the height information. This formulation also tended to find optimal solutions faster than other formulations.

@Article{Borges2015,
Title = {Effects of site productivity on forest harvest scheduling subject to green-up and maximum area restrictions},
Author = {Borges, Paulo and Martins, Isabel and Bergseng, Even and Eid, Tron and Gobakken, Terje},
Journal = {Scandinavian Journal of Forest Research},
Year = {2015},
Pages = {1-10},
Abstract = {ABSTRACTGreen-up requirements are of great interest for forests near cities since these forests are commonly used for recreational activities by the local population as well as for commercial forestry activities. We present three formulations to establish green-up requirements, based on a dynamic green-up approach and constructed by means of: (i) a predefined fixed length for the green-up time, (ii) a predefined variable length for the green-up time and (iii) height information produced by the growth simulator. Additionally, restrictions on harvested volume and maximum open areas were applied. All the green-up formulations were applied to five datasets comprising different initial forest conditions regarding age and site index distribution. Results show that higher net present values are obtained by the formulation that allow a predefined variable length for the green-up time and by using the height information from the growth simulator compared to the formulations using a predefined fixed length for the green-up time. The increase in NPV was most pronounced for the old forest datasets and varied between 4.23% and 8.15%. The optimal solution was always found when modeling the green-up requirement using the height information. This formulation also tended to find optimal solutions faster than other formulations.},
Doi = {10.1080/02827581.2015.1089931},
ISSN = {0282-7581},
Owner = {hanso},
Timestamp = {2016.03.01},
Type = {Journal Article},
Url = {http://dx.doi.org/10.1080/02827581.2015.1089931}
}

In this paper a novel semi-supervised SVM classifier is presented, specifically developed for tree species classification at individual tree crown (ITC) level. In ITC tree species classification, all the pixels belonging to an ITC should have the same label. This assumption is used in the learning of the proposed semi-supervised SVM classifier (ITC-S3VM). This method exploits the information contained in the unlabeled ITC samples in order to improve the classification accuracy of a standard SVM. The ITC-S3VM method can be easily implemented using freely available software libraries. The datasets used in this study include hyperspectral imagery and laser scanning data acquired over two boreal forest areas characterized by the presence of three information classes (Pine, Spruce, and Broadleaves). The experimental results quantify the effectiveness of the proposed approach, which provides classification accuracies significantly higher (from 2% to above 27%) than those obtained by the standard supervised SVM and by a state-of-the-art semi-supervised SVM (S3VM). Particularly, by reducing the number of training samples (i.e. from 100% to 25%, and from 100% to 5% for the two datasets, respectively) the proposed method still exhibits results comparable to the ones of a supervised SVM trained with the full available training set. This property of the method makes it particularly suitable for practical forest inventory applications in which collection of in situ information can be very expensive both in terms of cost and time.

@Article{Dalponte2015,
Title = {Semi-supervised SVM for individual tree crown species classification},
Author = {Dalponte, Michele and Ene, Liviu Theodor and Marconcini, Mattia and Gobakken, Terje and Næsset, Erik},
Journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
Year = {2015},
Pages = {77-87},
Volume = {110},
Abstract = {In this paper a novel semi-supervised SVM classifier is presented, specifically developed for tree species classification at individual tree crown (ITC) level. In ITC tree species classification, all the pixels belonging to an ITC should have the same label. This assumption is used in the learning of the proposed semi-supervised SVM classifier (ITC-S3VM). This method exploits the information contained in the unlabeled ITC samples in order to improve the classification accuracy of a standard SVM. The ITC-S3VM method can be easily implemented using freely available software libraries. The datasets used in this study include hyperspectral imagery and laser scanning data acquired over two boreal forest areas characterized by the presence of three information classes (Pine, Spruce, and Broadleaves). The experimental results quantify the effectiveness of the proposed approach, which provides classification accuracies significantly higher (from 2% to above 27%) than those obtained by the standard supervised SVM and by a state-of-the-art semi-supervised SVM (S3VM). Particularly, by reducing the number of training samples (i.e. from 100% to 25%, and from 100% to 5% for the two datasets, respectively) the proposed method still exhibits results comparable to the ones of a supervised SVM trained with the full available training set. This property of the method makes it particularly suitable for practical forest inventory applications in which collection of in situ information can be very expensive both in terms of cost and time.},
Doi = {http://dx.doi.org/10.1016/j.isprsjprs.2015.10.010},
ISSN = {0924-2716},
Keywords = {Tree species classification Semi-supervised classification Hyperspectral data SVM Individual tree crowns},
Owner = {hanso},
Timestamp = {2016.03.01},
Type = {Journal Article},
Url = {http://www.sciencedirect.com/science/article/pii/S0924271615002403}
}

This paper considers conditioning on the size of the samples observed in post-strata following a two-stage sampling design. We argue that it is reasonable to do so despite the complexity of the design. We derive an expression for the covariances among post-strata estimates resulting from secondary sampling units on the same primary sampling unit which reside in different post-strata. To motivate both issues we describe a two-stage LiDAR-assisted sample for aboveground biomass that was reported in Gregoire et al. (Can J For Res 41:83–95, 2011).

@Article{Gregoire2015,
Title = {Conditioning post-stratified inference following two-stage, equal-probability sampling},
Author = {Gregoire, Timothy G. and Ringvall, Anna H. and Ståhl, Göran and Næsset, Erik},
Journal = {Environmental and Ecological Statistics},
Year = {2015},
Number = {1},
Pages = {141-154},
Volume = {23},
Abstract = {This paper considers conditioning on the size of the samples observed in post-strata following a two-stage sampling design. We argue that it is reasonable to do so despite the complexity of the design. We derive an expression for the covariances among post-strata estimates resulting from secondary sampling units on the same primary sampling unit which reside in different post-strata. To motivate both issues we describe a two-stage LiDAR-assisted sample for aboveground biomass that was reported in Gregoire et al. (Can J For Res 41:83–95, 2011).},
Doi = {10.1007/s10651-015-0332-9},
ISSN = {1573-3009},
Owner = {hanso},
Timestamp = {2016.03.01},
Type = {Journal Article},
Url = {http://dx.doi.org/10.1007/s10651-015-0332-9}
}

<p>A large and growing body of evidence has demonstrated that airborne scanning light detection and ranging (lidar) systems can be an effective tool in measuring and monitoring above-ground forest tree biomass. However, the potential of lidar as an all-round tool for assisting in assessment of carbon (C) stocks in soil and non-tree vegetation components of the forest ecosystem has been given much less attention. Here we combine the use airborne small footprint scanning lidar with fine-scale spatial C data relating to vegetation and the soil surface to describe and contrast the size and spatial distribution of C pools within and among multilayered Norway spruce (<italic>Picea abies</italic>) stands. Predictor variables from lidar derived metrics delivered precise models of above- and below-ground tree C, which comprised the largest C pool in our study stands. We also found evidence that lidar canopy data correlated well with the variation in field layer C stock, consisting mainly of ericaceous dwarf shrubs and herbaceous plants. However, lidar metrics derived directly from understory echoes did not yield significant models. Furthermore, our results indicate that the variation in both the mosses and soil organic layer C stock plots appears less influenced by differences in stand structure properties than topographical gradients. By using topographical models from lidar ground returns we were able to establish a strong correlation between lidar data and the organic layer C stock at a stand level. Increasing the topographical resolution from plot averages (~2000 m<sup>2</sup>) towards individual grid cells (1 m<sup>2</sup>) did not yield consistent models. Our study demonstrates a connection between the size and distribution of different forest C pools and models derived from airborne lidar data, providing a foundation for future research concerning the use of lidar for assessing and monitoring boreal forest C.</p>

@Article{Kristensen2015,
Title = {Mapping Above- and Below-Ground Carbon Pools in Boreal Forests: The Case for Airborne Lidar},
Author = {Kristensen, Terje and Næsset, Erik and Ohlson, Mikael and Bolstad, Paul V. and Kolka, Randall},
Journal = {PLoS ONE},
Year = {2015},
Number = {10},
Pages = {e0138450},
Volume = {10},
Abstract = {<p>A large and growing body of evidence has demonstrated that airborne scanning light detection and ranging (lidar) systems can be an effective tool in measuring and monitoring above-ground forest tree biomass. However, the potential of lidar as an all-round tool for assisting in assessment of carbon (C) stocks in soil and non-tree vegetation components of the forest ecosystem has been given much less attention. Here we combine the use airborne small footprint scanning lidar with fine-scale spatial C data relating to vegetation and the soil surface to describe and contrast the size and spatial distribution of C pools within and among multilayered Norway spruce (<italic>Picea abies</italic>) stands. Predictor variables from lidar derived metrics delivered precise models of above- and below-ground tree C, which comprised the largest C pool in our study stands. We also found evidence that lidar canopy data correlated well with the variation in field layer C stock, consisting mainly of ericaceous dwarf shrubs and herbaceous plants. However, lidar metrics derived directly from understory echoes did not yield significant models. Furthermore, our results indicate that the variation in both the mosses and soil organic layer C stock plots appears less influenced by differences in stand structure properties than topographical gradients. By using topographical models from lidar ground returns we were able to establish a strong correlation between lidar data and the organic layer C stock at a stand level. Increasing the topographical resolution from plot averages (~2000 m<sup>2</sup>) towards individual grid cells (1 m<sup>2</sup>) did not yield consistent models. Our study demonstrates a connection between the size and distribution of different forest C pools and models derived from airborne lidar data, providing a foundation for future research concerning the use of lidar for assessing and monitoring boreal forest C.</p>},
Doi = {10.1371/journal.pone.0138450},
Owner = {hanso},
Timestamp = {2016.03.01},
Type = {Journal Article},
Url = {http://dx.doi.org/10.1371%2Fjournal.pone.0138450}
}

K. Lone, “Lidar, habitat structure and the ecology of ungulates in a landscape of fear,” PhD Thesis, 2015. [Bibtex][Download PDF]

@PhdThesis{Lone2015,
Title = {LiDAR, habitat structure and the ecology of ungulates in a landscape of fear},
Author = {Lone,K.},
School = {Norwegian University of Life Sciences},
Year = {2015},
Owner = {hanso},
Timestamp = {2016.03.02},
Url = {http://statisk.umb.no/ina/forskning/drgrader/2015-Lone.pdf}
}

The aim of this study was to examine whether pre-classification (stratification) of training data according to main tree species and stand development stage could improve the accuracy of species-specific forest attribute estimates compared to estimates without stratification using k-nearest neighbors (k-NN) imputations. The study included training data of 509 training plots and 80 validation plots from a conifer forest area in southeastern Norway. The results showed that stratification carried out by interpretation of aerial images did not improve the accuracy of the species-specific estimates due to stratification errors. The training data can of course be correctly stratified using field observations, but in the application phase the stratification entirely relies on auxiliary information with complete coverage over the entire area of interest which cannot be corrected. We therefore tried to improve the stratification using canopy height information from airborne laser scanning to discriminate between young and mature stands. The results showed that this approach slightly improved the accuracy of the k-NN predictions, especially for the main tree species (2.6% for spruce volume). Furthermore, if metrics from aerial images were used to discriminate between pine and spruce dominance in the mature plots, the accuracy of volume of pine was improved by 73.2% in pine-dominated stands while for spruce an adverse effect of 12.6% was observed.

@Article{Maltamo2015,
Title = {Using pre-classification to improve the accuracy of species-specific forest attribute estimates from airborne laser scanner data and aerial images},
Author = {Maltamo and Ørka and Bollandsås and Gobakken and Næsset},
Journal = {Scandinavian Journal of Forest Research},
Year = {2015},
Number = {4},
Pages = {336-345},
Volume = {30},
Abstract = {The aim of this study was to examine whether pre-classification (stratification) of training data according to main tree species and stand development stage could improve the accuracy of species-specific forest attribute estimates compared to estimates without stratification using k-nearest neighbors (k-NN) imputations. The study included training data of 509 training plots and 80 validation plots from a conifer forest area in southeastern Norway. The results showed that stratification carried out by interpretation of aerial images did not improve the accuracy of the species-specific estimates due to stratification errors. The training data can of course be correctly stratified using field observations, but in the application phase the stratification entirely relies on auxiliary information with complete coverage over the entire area of interest which cannot be corrected. We therefore tried to improve the stratification using canopy height information from airborne laser scanning to discriminate between young and mature stands. The results showed that this approach slightly improved the accuracy of the k-NN predictions, especially for the main tree species (2.6% for spruce volume). Furthermore, if metrics from aerial images were used to discriminate between pine and spruce dominance in the mature plots, the accuracy of volume of pine was improved by 73.2% in pine-dominated stands while for spruce an adverse effect of 12.6% was observed.},
Doi = {10.1080/02827581.2014.986520},
Keywords = {LiDAR discrimination forest inventory k-NN stratified data},
Owner = {hanso},
Timestamp = {2016.03.01},
Type = {Journal Article},
Url = {http://www.ingentaconnect.com/content/tandf/sfor/2015/00000030/00000004/art00009 http://dx.doi.org/10.1080/02827581.2014.986520}
}

E. W. Mauya, “Methods for estimating volume, biomass and tree species diversity using field inventory and airborne laser scanning in the tropical forests of tanzania,” PhD Thesis, 2015. [Bibtex][Download PDF]

@PhdThesis{Mauya2015,
Title = {Methods for estimating volume, biomass and tree species diversity using field inventory and airborne laser scanning in the tropical forests of Tanzania},
Author = {Mauya, E.W.},
School = {Norwegian University of Life Sciences},
Year = {2015},
Owner = {hanso},
Timestamp = {2016.03.02},
Url = {http://statisk.umb.no/ina/forskning/drgrader/2015-Mauya.pdf}
}

BACKGROUND:Airborne laser scanning (ALS) has emerged as one of the most promising remote sensing technologies for estimating aboveground biomass (AGB) in forests. Use of ALS data in area-based forest inventories relies on the development of statistical models that relate AGB and metrics derived from ALS. Such models are firstly calibrated on a sample of corresponding field- and ALS observations, and then used to predict AGB over the entire area covered by ALS data. Several statistical methods, both parametric and non-parametric, have been applied in ALS-based forest inventories, but studies that compare different methods in tropical forests in particular are few in number and less frequent than studies reported in temperate and boreal forests. We compared parametric and non-parametric methods, specifically linear mixed effects model (LMM) and k-nearest neighbor (k-NN).RESULTS:The results showed that the prediction accuracy obtained when using LMM was slightly better than when using the k-NN approach. Relative root mean square errors from the cross validation was 46.8% for the LMM and 58.1% for the k-NN. Post-stratification according to vegetation types improved the prediction accuracy of LMM more as compared to post-stratification by using land use types.CONCLUSION:Although there were differences in prediction accuracy between the two methods, their accuracies indicated that both of methods have potentials to be used for estimation of AGB using ALS data in the miombo woodlands. Future studies on effects of field plot size and the errors due to allometric models on the prediction accuracy are recommended.

@Article{Mauya2015a,
Title = {Modelling aboveground forest biomass using airborne laser scanner data in the miombo woodlands of Tanzania},
Author = {Mauya, Ernest and Ene, Liviu and Bollandsas, Ole and Gobakken, Terje and Naesset, Erik and Malimbwi, Rogers and Zahabu, Eliakimu},
Journal = {Carbon Balance and Management},
Year = {2015},
Number = {1},
Pages = {28},
Volume = {10},
Abstract = {BACKGROUND:Airborne laser scanning (ALS) has emerged as one of the most promising remote sensing technologies for estimating aboveground biomass (AGB) in forests. Use of ALS data in area-based forest inventories relies on the development of statistical models that relate AGB and metrics derived from ALS. Such models are firstly calibrated on a sample of corresponding field- and ALS observations, and then used to predict AGB over the entire area covered by ALS data. Several statistical methods, both parametric and non-parametric, have been applied in ALS-based forest inventories, but studies that compare different methods in tropical forests in particular are few in number and less frequent than studies reported in temperate and boreal forests. We compared parametric and non-parametric methods, specifically linear mixed effects model (LMM) and k-nearest neighbor (k-NN).RESULTS:The results showed that the prediction accuracy obtained when using LMM was slightly better than when using the k-NN approach. Relative root mean square errors from the cross validation was 46.8% for the LMM and 58.1% for the k-NN. Post-stratification according to vegetation types improved the prediction accuracy of LMM more as compared to post-stratification by using land use types.CONCLUSION:Although there were differences in prediction accuracy between the two methods, their accuracies indicated that both of methods have potentials to be used for estimation of AGB using ALS data in the miombo woodlands. Future studies on effects of field plot size and the errors due to allometric models on the prediction accuracy are recommended.},
ISSN = {1750-0680},
Owner = {hanso},
Timestamp = {2016.03.01},
Type = {Journal Article},
Url = {http://www.cbmjournal.com/content/10/1/28}
}

BACKGROUND:Airborne laser scanning (ALS) has recently emerged as a promising tool to acquire auxiliary information for improving aboveground biomass (AGB) estimation in sample-based forest inventories. Under design-based and model-assisted inferential frameworks, the estimation relies on a model that relates the auxiliary ALS metrics to AGB estimated on ground plots. The size of the field plots has been identified as one source of model uncertainty because of the so-called boundary effects which increases with decreasing plot size. Recent research in tropical forests has aimed to quantify the boundary effects on model prediction accuracy, but evidence of the consequences for the final AGB estimates is lacking. In this study we analyzed the effect of field plot size on model prediction accuracy and its implication when used in a model-assisted inferential framework.RESULTS:The results showed that the prediction accuracy of the model improved as the plot size increased. The adjusted R2 increased from 0.35 to 0.74 while the relative root mean square error decreased from 63.6 to 29.2%. Indicators of boundary effects were identified and confirmed to have significant effects on the model residuals. Variance estimates of model-assisted mean AGB relative to corresponding variance estimates of pure field-based AGB, decreased with increasing plot size in the range from 200 to 3000m2. The variance ratio of field-based estimates relative to model-assisted variance ranged from 1.7 to 7.7.CONCLUSIONS:This study showed that the relative improvement in precision of AGB estimation when increasing field-plot size, was greater for an ALS-assisted inventory compared to that of a pure field-based inventory.

@Article{Mauya2015b,
Title = {Effects of field plot size on prediction accuracy of aboveground biomass in airborne laser scanning-assisted inventories in tropical rain forests of Tanzania},
Author = {Mauya, Ernest and Hansen, Endre and Gobakken, Terje and Bollandsas, Ole and Malimbwi, Rogers and Naesset, Erik},
Journal = {Carbon Balance and Management},
Year = {2015},
Number = {1},
Pages = {10},
Volume = {10},
Abstract = {BACKGROUND:Airborne laser scanning (ALS) has recently emerged as a promising tool to acquire auxiliary information for improving aboveground biomass (AGB) estimation in sample-based forest inventories. Under design-based and model-assisted inferential frameworks, the estimation relies on a model that relates the auxiliary ALS metrics to AGB estimated on ground plots. The size of the field plots has been identified as one source of model uncertainty because of the so-called boundary effects which increases with decreasing plot size. Recent research in tropical forests has aimed to quantify the boundary effects on model prediction accuracy, but evidence of the consequences for the final AGB estimates is lacking. In this study we analyzed the effect of field plot size on model prediction accuracy and its implication when used in a model-assisted inferential framework.RESULTS:The results showed that the prediction accuracy of the model improved as the plot size increased. The adjusted R2 increased from 0.35 to 0.74 while the relative root mean square error decreased from 63.6 to 29.2%. Indicators of boundary effects were identified and confirmed to have significant effects on the model residuals. Variance estimates of model-assisted mean AGB relative to corresponding variance estimates of pure field-based AGB, decreased with increasing plot size in the range from 200 to 3000m2. The variance ratio of field-based estimates relative to model-assisted variance ranged from 1.7 to 7.7.CONCLUSIONS:This study showed that the relative improvement in precision of AGB estimation when increasing field-plot size, was greater for an ALS-assisted inventory compared to that of a pure field-based inventory.},
ISSN = {1750-0680},
Owner = {hanso},
Timestamp = {2016.03.01},
Type = {Journal Article},
Url = {http://www.cbmjournal.com/content/10/1/10}
}

Nearest neighbors techniques calculate predictions as linear combinations of observations for a selected number of population units in a sample that are most similar, or nearest, in a space of auxiliary variables to the population unit requiring the prediction. Nearest neighbors techniques have been shown to be particularly effective when used with forest inventory and remotely sensed data. Recent attention has focused on developing an underlying foundation consisting of diagnostic tools, inferential extensions, and techniques for optimization. For a study area in Norway, forest inventory and airborne laser scanning data were used with the k-Nearest Neighbors technique to estimate mean aboveground biomass per unit area. Optimization entailed reduction of the dimension of feature space, deletion of influential outliers, and selection of optimal weights for the weighted Euclidean distance metric. These optimization steps increased the proportion of variability explained in the reference set by as much as 20%, reduced confidence interval widths by as much as 35%, and produced standard errors that were as small as 3% of the estimate of the mean.

@Article{McRoberts2015b,
Title = {Optimizing the k-Nearest Neighbors technique for estimating forest aboveground biomass using airborne laser scanning data},
Author = {McRoberts, Ronald E. and Næsset, Erik and Gobakken, Terje},
Journal = {Remote Sensing of Environment},
Year = {2015},
Number = {0},
Pages = {13-22},
Volume = {163},
Abstract = {Nearest neighbors techniques calculate predictions as linear combinations of observations for a selected number of population units in a sample that are most similar, or nearest, in a space of auxiliary variables to the population unit requiring the prediction. Nearest neighbors techniques have been shown to be particularly effective when used with forest inventory and remotely sensed data. Recent attention has focused on developing an underlying foundation consisting of diagnostic tools, inferential extensions, and techniques for optimization. For a study area in Norway, forest inventory and airborne laser scanning data were used with the k-Nearest Neighbors technique to estimate mean aboveground biomass per unit area. Optimization entailed reduction of the dimension of feature space, deletion of influential outliers, and selection of optimal weights for the weighted Euclidean distance metric. These optimization steps increased the proportion of variability explained in the reference set by as much as 20%, reduced confidence interval widths by as much as 35%, and produced standard errors that were as small as 3% of the estimate of the mean.},
Doi = {http://dx.doi.org/10.1016/j.rse.2015.02.026},
ISSN = {0034-4257},
Keywords = {Distance metric Precision},
Owner = {hanso},
Timestamp = {2016.03.01},
Type = {Journal Article},
Url = {http://www.sciencedirect.com/science/article/pii/S0034425715000851}
}

Remote sensing-based change estimation typically takes two forms. Indirect estimation entails constructing models of the relationship between the response variable of interest and remotely sensed auxiliary variables at two times and then estimating change as the differences in the model predictions for the two times. Direct estimation entails constructing models of change directly using observations of change in the response and the remotely sensed auxiliary variables for two dates. The direct method is generally preferred, although few statistically rigorous comparisons have been reported. This study focused on statistically rigorous, indirect and direct estimation of biomass change using forest inventory and airborne laser scanning (ALS) data for a Norwegian study area. Three sets of statistical estimators were used: simple random sampling estimators, indirect model-assisted regression estimators, and direct model-assisted regression estimators. In addition, three modeling approaches were used to support the direct model-assisted estimators. The study produced four relevant findings. First, use of the ALS auxiliary information greatly increased the precision of change estimates, regardless of whether indirect or direct methods were used. Second, contrary to previously reported results, the indirect method produced greater precision for the study area mean than the traditional direct method. Third, the direct method that used models whose predictor variables were selected in pairs but with separate coefficient estimates and models whose predictor variables were selected without regard to pairing produced the greatest precision. Finally, greater emphasis should be placed on the effects of model extrapolations for values of independent variables in the population that are beyond the range of the variables in the sample.

@Article{McRoberts2015a,
Title = {Indirect and direct estimation of forest biomass change using forest inventory and airborne laser scanning data},
Author = {McRoberts, Ronald E. and Næsset, Erik and Gobakken, Terje and Bollandsås, Ole Martin},
Journal = {Remote Sensing of Environment},
Year = {2015},
Number = {0},
Pages = {36-42},
Volume = {164},
Abstract = {Remote sensing-based change estimation typically takes two forms. Indirect estimation entails constructing models of the relationship between the response variable of interest and remotely sensed auxiliary variables at two times and then estimating change as the differences in the model predictions for the two times. Direct estimation entails constructing models of change directly using observations of change in the response and the remotely sensed auxiliary variables for two dates. The direct method is generally preferred, although few statistically rigorous comparisons have been reported. This study focused on statistically rigorous, indirect and direct estimation of biomass change using forest inventory and airborne laser scanning (ALS) data for a Norwegian study area. Three sets of statistical estimators were used: simple random sampling estimators, indirect model-assisted regression estimators, and direct model-assisted regression estimators. In addition, three modeling approaches were used to support the direct model-assisted estimators. The study produced four relevant findings. First, use of the ALS auxiliary information greatly increased the precision of change estimates, regardless of whether indirect or direct methods were used. Second, contrary to previously reported results, the indirect method produced greater precision for the study area mean than the traditional direct method. Third, the direct method that used models whose predictor variables were selected in pairs but with separate coefficient estimates and models whose predictor variables were selected without regard to pairing produced the greatest precision. Finally, greater emphasis should be placed on the effects of model extrapolations for values of independent variables in the population that are beyond the range of the variables in the sample.},
Doi = {http://dx.doi.org/10.1016/j.rse.2015.02.018},
ISSN = {0034-4257},
Keywords = {Simple random sampling estimator Stratified estimator Model-assisted regression estimator},
Owner = {hanso},
Timestamp = {2016.03.01},
Type = {Journal Article},
Url = {http://www.sciencedirect.com/science/article/pii/S0034425715000772}
}

Remotely sensed data from airborne laser scanning (ALS) and interferometric synthetic aperture radar (InSAR) can greatly improve the precision of estimates of forest resource parameters such as mean biomass and biomass change per unit area. Field plots are typically used to construct models that relate the variable of interest to explanatory variables derived from the remotely sensed data. The models may then be used in combination with the field plots to provide estimates for a geographical area of interest with corresponding estimates of precision using model-assisted estimators. Previous studies have shown that field plot sizes found suitable for pure field surveys may be sub-optimal for use in combination with remotely sensed data. Plot boundary effects, co-registration problems, and misalignment problems favor larger plots because the relative impact of these effects on the models of relationships may decline by increasing plot size. In a case study in a small boreal forest area in southeastern Norway (852.6 ha) a probability sample of 145 field plots was measured twice over an 11 year period (1998/1999 and 2010). For each plot, field measurements were recorded for two plot sizes (200 m2 and 300/400 m2). Corresponding multitemporal ALS (1999 and 2010) and InSAR data (2000 and 2011) were also available. Biomass for each of the two measurement dates as well as biomass change were modeled for all plot sizes separately using explanatory variables from the ALS and InSAR data, respectively. Biomass change was estimated using model-assisted estimators. Separate estimates were obtained for different methods for estimation of change, like the indirect method (difference between predictions of biomass for each of the two measurement dates) and the direct method (direct prediction of change). Relative efficiency (RE) was calculated by dividing the variance obtained for a pure field-based change estimate by the variance of a corresponding estimate using the model-assisted approach. For ALS, the RE values ranged between 7.5 and 15.0, indicating that approximately 7.5–15.0 as many field plots would be required for a pure field-based estimate to provide the same precision as an ALS-assisted estimate. For InSAR, RE ranged between 1.8 and 2.5. The direct estimation method showed greater REs than the indirect method for both remote sensing technologies. There was clearly a trend of improved RE of the model-assisted estimates by increasing plot size. For ALS and the direct estimation method RE increased from 9.8 for 200 m2 plots to 15.0 for 400 m2 plots. Similar trends of increasing RE with plot size were observed for InSAR. ALS showed on average 3.2–6.0 times greater RE values than InSAR. Because remote sensing can contribute to improved precision of estimates, sample plot size is a prominent design issue in future sample surveys which should be considered with due attention to the great benefits that can be achieved when using remote sensing if the plot size reflects the specific challenges arising from use of remote sensing in the estimation. That is especially the case in the tropics where field resources may be scarce and inaccessibility and poor infrastructure hamper field work.

@Article{Naesset2015,
Title = {The effects of field plot size on model-assisted estimation of aboveground biomass change using multitemporal interferometric SAR and airborne laser scanning data},
Author = {Næsset, Erik and Bollandsås, Ole Martin and Gobakken, Terje and Solberg, Svein and McRoberts, Ronald E.},
Journal = {Remote Sensing of Environment},
Year = {2015},
Pages = {252-264},
Volume = {168},
Abstract = {Remotely sensed data from airborne laser scanning (ALS) and interferometric synthetic aperture radar (InSAR) can greatly improve the precision of estimates of forest resource parameters such as mean biomass and biomass change per unit area. Field plots are typically used to construct models that relate the variable of interest to explanatory variables derived from the remotely sensed data. The models may then be used in combination with the field plots to provide estimates for a geographical area of interest with corresponding estimates of precision using model-assisted estimators. Previous studies have shown that field plot sizes found suitable for pure field surveys may be sub-optimal for use in combination with remotely sensed data. Plot boundary effects, co-registration problems, and misalignment problems favor larger plots because the relative impact of these effects on the models of relationships may decline by increasing plot size. In a case study in a small boreal forest area in southeastern Norway (852.6 ha) a probability sample of 145 field plots was measured twice over an 11 year period (1998/1999 and 2010). For each plot, field measurements were recorded for two plot sizes (200 m2 and 300/400 m2). Corresponding multitemporal ALS (1999 and 2010) and InSAR data (2000 and 2011) were also available. Biomass for each of the two measurement dates as well as biomass change were modeled for all plot sizes separately using explanatory variables from the ALS and InSAR data, respectively. Biomass change was estimated using model-assisted estimators. Separate estimates were obtained for different methods for estimation of change, like the indirect method (difference between predictions of biomass for each of the two measurement dates) and the direct method (direct prediction of change). Relative efficiency (RE) was calculated by dividing the variance obtained for a pure field-based change estimate by the variance of a corresponding estimate using the model-assisted approach. For ALS, the RE values ranged between 7.5 and 15.0, indicating that approximately 7.5–15.0 as many field plots would be required for a pure field-based estimate to provide the same precision as an ALS-assisted estimate. For InSAR, RE ranged between 1.8 and 2.5. The direct estimation method showed greater REs than the indirect method for both remote sensing technologies. There was clearly a trend of improved RE of the model-assisted estimates by increasing plot size. For ALS and the direct estimation method RE increased from 9.8 for 200 m2 plots to 15.0 for 400 m2 plots. Similar trends of increasing RE with plot size were observed for InSAR. ALS showed on average 3.2–6.0 times greater RE values than InSAR. Because remote sensing can contribute to improved precision of estimates, sample plot size is a prominent design issue in future sample surveys which should be considered with due attention to the great benefits that can be achieved when using remote sensing if the plot size reflects the specific challenges arising from use of remote sensing in the estimation. That is especially the case in the tropics where field resources may be scarce and inaccessibility and poor infrastructure hamper field work.},
Doi = {http://dx.doi.org/10.1016/j.rse.2015.07.002},
ISSN = {0034-4257},
Keywords = {Biomass estimation Biomass change estimation ALS Tandem-X InSAR Model-assisted estimation Precision Relative efficiency Plot size Plot boundary effect},
Owner = {hanso},
Timestamp = {2016.03.01},
Type = {Journal Article},
Url = {http://www.sciencedirect.com/science/article/pii/S0034425715300596}
}

BACKGROUND:REDD+ implementation requires establishment of a system for measuring, reporting and verification (MRV) of forest carbon changes. A challenge for MRV is the lack of satellite based methods that can track not only deforestation, but also degradation and forest growth, as well as a lack of historical data that can serve as a basis for a reference emission level. Working in a miombo woodland in Tanzania, we here aim at demonstrating a novel 3D satellite approach based on interferometric processing of radar imagery (InSAR).RESULTS:Forest carbon changes are derived from changes in the forest canopy height obtained from InSAR, i.e. decreases represent carbon loss from logging and increases represent carbon sequestration through forest growth. We fitted a model of above-ground biomass (AGB) against InSAR height, and used this to convert height changes to biomass and carbon changes. The relationship between AGB and InSAR height was weak, as the individual plots were widely scattered around the model fit. However, we consider the approach to be unique and feasible for large-scale MRV efforts in REDD+ because the low accuracy was attributable partly to small plots and other limitations in the data set, and partly to a random pixel-to-pixel variation in trunk forms. Further processing of the InSAR data provides data on the categories of forest change.The combination of InSAR data from the Shuttle RADAR Topography Mission (SRTM) and the TanDEM-X satellite mission provided both historic baseline of change for the period 2000-2011, as well as annual change 2011-2012.CONCLUSIONS:A 3D data set from InSAR is a promising tool for MRV in REDD+. The temporal changes seen by InSAR data corresponded well with, but largely supplemented, the changes derived from Landsat data.

@Article{Solberg2015,
Title = {Monitoring forest carbon in a Tanzanian woodland using interferometric SAR: a novel methodology for REDD+},
Author = {Solberg, Svein and Gizachew, Belachew and Naesset, Erik and Gobakken, Terje and Bollandsas, Ole and Mauya, Ernest and Olsson, Hakan and Malimbwi, Rogers and Zahabu, Eliakimu},
Journal = {Carbon Balance and Management},
Year = {2015},
Number = {1},
Pages = {14},
Volume = {10},
Abstract = {BACKGROUND:REDD+ implementation requires establishment of a system for measuring, reporting and verification (MRV) of forest carbon changes. A challenge for MRV is the lack of satellite based methods that can track not only deforestation, but also degradation and forest growth, as well as a lack of historical data that can serve as a basis for a reference emission level. Working in a miombo woodland in Tanzania, we here aim at demonstrating a novel 3D satellite approach based on interferometric processing of radar imagery (InSAR).RESULTS:Forest carbon changes are derived from changes in the forest canopy height obtained from InSAR, i.e. decreases represent carbon loss from logging and increases represent carbon sequestration through forest growth. We fitted a model of above-ground biomass (AGB) against InSAR height, and used this to convert height changes to biomass and carbon changes. The relationship between AGB and InSAR height was weak, as the individual plots were widely scattered around the model fit. However, we consider the approach to be unique and feasible for large-scale MRV efforts in REDD+ because the low accuracy was attributable partly to small plots and other limitations in the data set, and partly to a random pixel-to-pixel variation in trunk forms. Further processing of the InSAR data provides data on the categories of forest change.The combination of InSAR data from the Shuttle RADAR Topography Mission (SRTM) and the TanDEM-X satellite mission provided both historic baseline of change for the period 2000-2011, as well as annual change 2011-2012.CONCLUSIONS:A 3D data set from InSAR is a promising tool for MRV in REDD+. The temporal changes seen by InSAR data corresponded well with, but largely supplemented, the changes derived from Landsat data.},
ISSN = {1750-0680},
Owner = {hanso},
Timestamp = {2016.03.01},
Type = {Journal Article},
Url = {http://www.cbmjournal.com/content/10/1/14}
}

BACKGROUND:Anthropogenic uses of fire play a key role in regulating fire regimes in African savannas. These fires contribute the highest proportion of the globally burned area, substantial biomass burning emissions and threaten maintenance and enhancement of carbon stocks. An understanding of fire regimes at local scales is required for the estimation and prediction of the contributionof these fires to the global carbon cycle and for fire management. We assessed the spatio-temporal distribution of fires in miombo woodlands of Tanzania, utilizing the MODIS active fire product and Landsat satellite images for the past ~40years.RESULTS:Our results show that up to 50.6% of the woodland area is affected by fire each year. An early and a late dry season peak in wetter and drier miombo, respectively, characterize the annual fire season. Wetter miombo areas have higher fire activity within a shorter annual fire season and have shorter return intervals. The fire regime is characterized by small-sized fires, with a higher ratio of small than large burned areas in the frequency-size distribution (beta=2.16+/-0.04). Large-sized fires are rare, and occur more frequently in drier than in wetter miombo. Both fire prevalence and burned extents have decreased in the past decade. At a large scale, more than half of the woodland area has less than 2years of fire return intervals, which prevent the occurrence of large intense fires.CONCLUSION:The sizes of fires, season of burning and spatial extent of occurrence are generally consistent across time, at the scale of the current analysis. Where traditional use of fire is restricted, a reassessment of fire management strategies may be required, if sustainability of tree cover is a priority. In such cases, there is a need to combine traditional and contemporary fire management practices.

@Article{Tarimo2015,
Title = {Spatial distribution of temporal dynamics in anthropogenic fires in miombo savanna woodlands of Tanzania},
Author = {Tarimo, Beatrice and Dick, Oystein and Gobakken, Terje and Totland, Orjan},
Journal = {Carbon Balance and Management},
Year = {2015},
Number = {1},
Pages = {18},
Volume = {10},
Abstract = {BACKGROUND:Anthropogenic uses of fire play a key role in regulating fire regimes in African savannas. These fires contribute the highest proportion of the globally burned area, substantial biomass burning emissions and threaten maintenance and enhancement of carbon stocks. An understanding of fire regimes at local scales is required for the estimation and prediction of the contributionof these fires to the global carbon cycle and for fire management. We assessed the spatio-temporal distribution of fires in miombo woodlands of Tanzania, utilizing the MODIS active fire product and Landsat satellite images for the past ~40years.RESULTS:Our results show that up to 50.6% of the woodland area is affected by fire each year. An early and a late dry season peak in wetter and drier miombo, respectively, characterize the annual fire season. Wetter miombo areas have higher fire activity within a shorter annual fire season and have shorter return intervals. The fire regime is characterized by small-sized fires, with a higher ratio of small than large burned areas in the frequency-size distribution (beta=2.16+/-0.04). Large-sized fires are rare, and occur more frequently in drier than in wetter miombo. Both fire prevalence and burned extents have decreased in the past decade. At a large scale, more than half of the woodland area has less than 2years of fire return intervals, which prevent the occurrence of large intense fires.CONCLUSION:The sizes of fires, season of burning and spatial extent of occurrence are generally consistent across time, at the scale of the current analysis. Where traditional use of fire is restricted, a reassessment of fire management strategies may be required, if sustainability of tree cover is a priority. In such cases, there is a need to combine traditional and contemporary fire management practices.},
ISSN = {1750-0680},
Owner = {hanso},
Timestamp = {2016.03.01},
Type = {Journal Article},
Url = {http://www.cbmjournal.com/content/10/1/18}
}

The aim of this study was to explore the ability of estimating change in total aboveground biomass (AGB) in young forests using multi-temporal airborne laser scanner data. A field data-set covering 11 growth seasons of 39 circular plots of size 200 m<sup>2</sup> from young forest in south-eastern Norway was used in the analyses. Different approaches for prediction of the AGB change were tested. One approach was based on modeling AGB for each point in time and predicting change as the difference between separate AGB predictions. We also tested two approaches based on modeling and predicting change directly, and two approaches where growth/reduction rates were modeled and used in prediction. The approach where change was predicted as a difference between biomass predictions seemed to yield the best results (root mean square error [RMSE] 14.8%). The other approaches yielded results that were similar in terms of RMSE, except for the approach where AGB change was predicted using a growth rate. The results indicate that prediction of change as a difference between AGB predictions works satisfactory for a wide range of forest conditions, but that the direct approaches can perform better in some cases.

@Article{Oekseter2015a,
Title = {Modeling and predicting aboveground biomass change in young forest using multi-temporal airborne laser scanner data},
Author = {Økseter and Bollandsås and Gobakken and Næsset},
Journal = {Scandinavian Journal of Forest Research},
Year = {2015},
Number = {5},
Pages = {458-469},
Volume = {30},
Abstract = {The aim of this study was to explore the ability of estimating change in total aboveground biomass (AGB) in young forests using multi-temporal airborne laser scanner data. A field data-set covering 11 growth seasons of 39 circular plots of size 200 m<sup>2</sup> from young forest in south-eastern Norway was used in the analyses. Different approaches for prediction of the AGB change were tested. One approach was based on modeling AGB for each point in time and predicting change as the difference between separate AGB predictions. We also tested two approaches based on modeling and predicting change directly, and two approaches where growth/reduction rates were modeled and used in prediction. The approach where change was predicted as a difference between biomass predictions seemed to yield the best results (root mean square error [RMSE] 14.8%). The other approaches yielded results that were similar in terms of RMSE, except for the approach where AGB change was predicted using a growth rate. The results indicate that prediction of change as a difference between AGB predictions works satisfactory for a wide range of forest conditions, but that the direct approaches can perform better in some cases.},
Doi = {10.1080/02827581.2015.1024733},
Keywords = {Norway airborne laser scanning biomass change carbon stock forest growth multi-temporal data},
Owner = {hanso},
Timestamp = {2016.03.01},
Type = {Journal Article},
Url = {http://www.ingentaconnect.com/content/tandf/sfor/2015/00000030/00000005/art00009 http://dx.doi.org/10.1080/02827581.2015.1024733}
}

Tree species classification accuracy at the individual tree crown (ITC) level depends on many factors, among which in this paper we analyzed: i) the remote sensing data used for the ITC delineation process carried out prior to the classification, and ii) the pixels considered inside each ITC during the classification process. These two factors were analyzed on the ITC level classification accuracy of boreal tree species (Pine, Spruce and Broadleaves), considering two remote sensing data types: hyperspectral and airborne laser scanning (ALS). ITCs were delineated automatically on ALS and on hyperspectral data. A manual ITC delineation was used as reference in the analysis. The pixel level classification was performed on the hyperspectral bands using a non-linear support vector machine. The classification at ITC level was obtained by applying a majority voting rule to the classified pixels confined by each ITC. The results showed that ITCs automatically delineated from hyperspectral data were usually smaller than those from ALS, and the tree detection rate for hyperspectral data was much lower compared to ALS data (28.4 versus 48.5%). Regarding the classification results, using only manually delineated ITCs a kappa accuracy of 0.89 was obtained, while using only automatically delineated ITCs from hyperspectral or ALS data reduced the kappa values to 0.79 and 0.76, respectively. Slightly different results were achieved using semi-automatic approaches based on both manual and automatically delineated ITC (0.81 and 0.74, respectively). A selection of only certain pixels inside each ITC improved the classification accuracy from 1 to 7 percentage points. A selection based on the spectral values of the pixels was found more influential than the one based on the ALS-derived canopy height model. The best results were obtained after a selection based on the spectral values in the bands in the blue region of the spectrum using either the Otsu method or an ad-hoc percentile-based thresholding method.

@Article{Dalponte2014a,
Title = {Tree crown delineation and tree species classification in boreal forests using hyperspectral and ALS data},
Author = {Dalponte, Michele and Ørka, Hans Ole and Ene, Liviu Theodor and Gobakken, Terje and Næsset, Erik},
Journal = {Remote Sensing of Environment},
Year = {2014},
Number = {0},
Pages = {306-317},
Volume = {140},
Abstract = {Tree species classification accuracy at the individual tree crown (ITC) level depends on many factors, among which in this paper we analyzed: i) the remote sensing data used for the ITC delineation process carried out prior to the classification, and ii) the pixels considered inside each ITC during the classification process. These two factors were analyzed on the ITC level classification accuracy of boreal tree species (Pine, Spruce and Broadleaves), considering two remote sensing data types: hyperspectral and airborne laser scanning (ALS). ITCs were delineated automatically on ALS and on hyperspectral data. A manual ITC delineation was used as reference in the analysis. The pixel level classification was performed on the hyperspectral bands using a non-linear support vector machine. The classification at ITC level was obtained by applying a majority voting rule to the classified pixels confined by each ITC. The results showed that ITCs automatically delineated from hyperspectral data were usually smaller than those from ALS, and the tree detection rate for hyperspectral data was much lower compared to ALS data (28.4 versus 48.5%). Regarding the classification results, using only manually delineated ITCs a kappa accuracy of 0.89 was obtained, while using only automatically delineated ITCs from hyperspectral or ALS data reduced the kappa values to 0.79 and 0.76, respectively. Slightly different results were achieved using semi-automatic approaches based on both manual and automatically delineated ITC (0.81 and 0.74, respectively). A selection of only certain pixels inside each ITC improved the classification accuracy from 1 to 7 percentage points. A selection based on the spectral values of the pixels was found more influential than the one based on the ALS-derived canopy height model. The best results were obtained after a selection based on the spectral values in the bands in the blue region of the spectrum using either the Otsu method or an ad-hoc percentile-based thresholding method.},
Keywords = {ALS Hyperspectral Tree species classification Individual tree crowns Delineation Forest inventory Post classification},
Owner = {hanso},
Timestamp = {2014.10.14}
}

Capsule Variables obtained from airborne laser-scanning (ALS) enabled slight or fair predictions of bird presence, and including multispectral data further improved predictions slightly.Aims To assess the usefulness of ALS as a tool for predicting species richness and single-species presence, and to investigate if including information from multispectral aerial images further improved predictability of bird presence.Methods Bird presence data were sampled in a Norwegian boreal forest reserve. Prediction models were developed for species richness and presence of the eight most abundant species by the use of two different modelling approaches: generalized linear models and the machine learning method random forest. Predictor variables were descriptors of three-dimensional forest structure obtained by ALS, and descriptors of tree species composition obtained from multispectral aerial images.Results Cross-validation of the prediction models indicated overall slight or fair predictive capability. Best predictions were obtained for Goldcrest, Wren, and Willow Warbler. Inclusion of spectral variables derived from the aerial imagery slightly improved the predictive performance of several models, most notably for Willow Warbler.Conclusion We suggest that predictability of species richness and presence of single bird species can be improved by better matching of the scale of recording for birds and the predictor variables obtained by remote sensing.

@Article{Eldegard2014,
Title = {Modelling bird richness and bird species presence in a boreal forest reserve using airborne laser-scanning and aerial images},
Author = {Eldegard, Katrine and Dirksen, John Wirkola and Ørka, Hans Ole and Halvorsen, Rune and Næsset, Erik and Gobakken, Terje and Ohlson, Mikael},
Journal = {Bird Study},
Year = {2014},
Number = {2},
Pages = {204-219},
Volume = {61},
Abstract = {Capsule Variables obtained from airborne laser-scanning (ALS) enabled slight or fair predictions of bird presence, and including multispectral data further improved predictions slightly.Aims To assess the usefulness of ALS as a tool for predicting species richness and single-species presence, and to investigate if including information from multispectral aerial images further improved predictability of bird presence.Methods Bird presence data were sampled in a Norwegian boreal forest reserve. Prediction models were developed for species richness and presence of the eight most abundant species by the use of two different modelling approaches: generalized linear models and the machine learning method random forest. Predictor variables were descriptors of three-dimensional forest structure obtained by ALS, and descriptors of tree species composition obtained from multispectral aerial images.Results Cross-validation of the prediction models indicated overall slight or fair predictive capability. Best predictions were obtained for Goldcrest, Wren, and Willow Warbler. Inclusion of spectral variables derived from the aerial imagery slightly improved the predictive performance of several models, most notably for Willow Warbler.Conclusion We suggest that predictability of species richness and presence of single bird species can be improved by better matching of the scale of recording for birds and the predictor variables obtained by remote sensing.},
Owner = {hanso},
Timestamp = {2014.10.14}
}

Recent development in aerial digital cameras and software facilitate the photogrammetric point cloud as a new data source in forest management planning. A total of 151 field training plots were distributed systematically within three predefined strata in a 852.6 ha study area located in the boreal forest in southeastern Norway. Stratum-specific regression models were fitted for six studied biophysical forest characteristics. The explanatory variables were various canopy height and canopy density metrics derived by means of photogrammetric matching of aerial images and small-footprint laser scanning. The ground sampling distance was 17 cm for the images and the airborne laser scanning (ALS) pulse density was 7.4 points m?2. Resampled images were assessed to mimic acquisitions at higher flying altitudes. The digital terrain model derived from the ALS data was used to represent the ground surface. The results were evaluated using 63 independent test stands. When estimating height in young forest and mature forest on poor sites, the root mean square error (RMSE) values were slightly better using data from image matching compared to ALS. However, for all other combinations of biophysical forest characteristics and strata, better results were obtained using ALS data. In general, the best results were found using the highest image resolution.

@Article{Gobakken2014,
Title = {Comparing biophysical forest characteristics estimated from photogrammetric matching of aerial images and airborne laser scanning data},
Author = {Gobakken, Terje and Bollandsås, Ole Martin and Næsset, Erik},
Journal = {Scandinavian Journal of Forest Research},
Year = {2014},
Pages = {1-14},
Abstract = {Recent development in aerial digital cameras and software facilitate the photogrammetric point cloud as a new data source in forest management planning. A total of 151 field training plots were distributed systematically within three predefined strata in a 852.6 ha study area located in the boreal forest in southeastern Norway. Stratum-specific regression models were fitted for six studied biophysical forest characteristics. The explanatory variables were various canopy height and canopy density metrics derived by means of photogrammetric matching of aerial images and small-footprint laser scanning. The ground sampling distance was 17 cm for the images and the airborne laser scanning (ALS) pulse density was 7.4 points m?2. Resampled images were assessed to mimic acquisitions at higher flying altitudes. The digital terrain model derived from the ALS data was used to represent the ground surface. The results were evaluated using 63 independent test stands. When estimating height in young forest and mature forest on poor sites, the root mean square error (RMSE) values were slightly better using data from image matching compared to ALS. However, for all other combinations of biophysical forest characteristics and strata, better results were obtained using ALS data. In general, the best results were found using the highest image resolution.},
Owner = {hanso},
Timestamp = {2014.10.14}
}

Remote sensing plays an important role within the field of forest inventory. Airborne laser scanning (ALS) has become an effective tool for acquiring forest inventory data. In most ALS-based forest inventories, accurately positioned field plots are used in the process of relating ALS data to field-observed biophysical properties. The geo-referencing of these field plots is typically carried out by means of differential global navigation satellite systems (dGNSS), and often relies on logging times of 15?20 min to ensure adequate accuracy under different forest conditions. Terrestrial laser scanning (TLS) has been proposed as a possible tool for collection of field data in forest inventories and can facilitate rapid acquisition of these data. In the present study, a novel method for co-registration of TLS and ALS data by posterior analysis of remote-sensing data ? rather than using dGNSS ? was proposed and then tested on 71 plots in a boreal forest. The method relies on an initial position obtained with a recreational-grade GPS receiver, in addition to analysis of the ALS and TLS data. First, individual tree positions were derived from the remote-sensing data. A search algorithm was then used to find the best match for the TLS-derived trees among the ALS-derived trees within a search area, defined relative to the initial position. The accuracy of co-registration was assessed by comparison with an accurately measured reference position. With a search radius of 25Ã‚Â m and using low-density ALS data (0.7 points m?2), 82% and 51% of the TLS scans were co-registered with positional errors within 1Ã‚Â m and 0.5Ã‚Â m, respectively. By using ALS data of medium density (7.5 points m?2), 87% and 78% of the scans were co-registered with errors within 1Ã‚Â m and 0.5Ã‚Â m of the reference position, respectively. These results are promising and the method can facilitate rapid acquisition and geo-referencing of field data. Robust methods to identify and handle erroneous matches are, however, required before it is suitable for operational use.

@Article{Hauglin2014a,
Title = {Geo-referencing forest field plots by co-registration of terrestrial and airborne laser scanning data},
Author = {Hauglin, Marius and Lien, Vegard and Næsset, Erik and Gobakken, Terje},
Journal = {International Journal of Remote Sensing},
Year = {2014},
Number = {9},
Pages = {3135-3149},
Volume = {35},
Abstract = {Remote sensing plays an important role within the field of forest inventory. Airborne laser scanning (ALS) has become an effective tool for acquiring forest inventory data. In most ALS-based forest inventories, accurately positioned field plots are used in the process of relating ALS data to field-observed biophysical properties. The geo-referencing of these field plots is typically carried out by means of differential global navigation satellite systems (dGNSS), and often relies on logging times of 15?20 min to ensure adequate accuracy under different forest conditions. Terrestrial laser scanning (TLS) has been proposed as a possible tool for collection of field data in forest inventories and can facilitate rapid acquisition of these data. In the present study, a novel method for co-registration of TLS and ALS data by posterior analysis of remote-sensing data ? rather than using dGNSS ? was proposed and then tested on 71 plots in a boreal forest. The method relies on an initial position obtained with a recreational-grade GPS receiver, in addition to analysis of the ALS and TLS data. First, individual tree positions were derived from the remote-sensing data. A search algorithm was then used to find the best match for the TLS-derived trees among the ALS-derived trees within a search area, defined relative to the initial position. The accuracy of co-registration was assessed by comparison with an accurately measured reference position. With a search radius of 25Ã‚Â m and using low-density ALS data (0.7 points m?2), 82% and 51% of the TLS scans were co-registered with positional errors within 1Ã‚Â m and 0.5Ã‚Â m, respectively. By using ALS data of medium density (7.5 points m?2), 87% and 78% of the scans were co-registered with errors within 1Ã‚Â m and 0.5Ã‚Â m of the reference position, respectively. These results are promising and the method can facilitate rapid acquisition and geo-referencing of field data. Robust methods to identify and handle erroneous matches are, however, required before it is suitable for operational use.},
Owner = {hanso},
Timestamp = {2014.10.14}
}

The theory of predation risk effects predicts behavioral responses in prey when risk of predation is not homogenous in space and time. Prey species are often faced with a tradeoff between food and safety in situations where food availability and predation risk peak in the same habitat type. Determining the optimal strategy becomes more complex if predators with different hunting mode create contrasting landscapes of risk, but this has rarely been documented in vertebrates. Roe deer in southeastern Norway face predation risk from lynx, as well as hunting by humans. These two predators differ greatly in their hunting methods. The predation risk from lynx, an efficient stalk-and-ambush predator is expected to be higher in areas with dense understory vegetation, while predation risk from human hunters is expected to be higher where visual sight lines are longer. Based on field observations and airborne LiDAR data from 71 lynx predation sites, 53 human hunting sites, 132 locations from 15 GPS-marked roe deer, and 36 roe deer pellet locations from a regional survey, we investigated how predation risk was related to terrain attributes and vegetation classes/structure. As predicted, we found that increasing cover resulted in a contrasting lower predation risk from humans and higher predation risk from lynx. Greater terrain ruggedness increased the predation risk from both predators. Hence, multiple predators may create areas of contrasting risk as well as double risk in the same landscape. Our study highlights the complexity of predatorÃ¯Â¿Â½Ã¯Â¿Â½Ã¯Â¿Â½prey relationship in a multiple predator setting.SynthesisIn this study of risk effects in a multi-predator context, LiDAR data were used to quantify cover in the habitat and relate it to vulnerability to predation in a boreal forest. We found that lynx and human hunters superimpose generally contrasting landscapes of fear on a common prey species, but also identified double-risk zones. Since the benefit of anti-predator responses depends on the combined risk from all predators, it is necessary to consider complete predator assemblages to understand the potential for and occurrence of risk effects across study systems.

@Article{Lone2014a,
Title = {Living and dying in a multi-predator landscape of fear: roe deer are squeezed by contrasting pattern of predation risk imposed by lynx and humans},
Author = {Lone, Karen and Loe, Leif Egil and Gobakken, Terje and Linnell, John D. C. and Odden, John and Remmen, Jørgen and Mysterud, Atle},
Journal = {Oikos},
Year = {2014},
Number = {6},
Pages = {641--651},
Volume = {123},
Abstract = {The theory of predation risk effects predicts behavioral responses in prey when risk of predation is not homogenous in space and time. Prey species are often faced with a tradeoff between food and safety in situations where food availability and predation risk peak in the same habitat type. Determining the optimal strategy becomes more complex if predators with different hunting mode create contrasting landscapes of risk, but this has rarely been documented in vertebrates. Roe deer in southeastern Norway face predation risk from lynx, as well as hunting by humans. These two predators differ greatly in their hunting methods. The predation risk from lynx, an efficient stalk-and-ambush predator is expected to be higher in areas with dense understory vegetation, while predation risk from human hunters is expected to be higher where visual sight lines are longer. Based on field observations and airborne LiDAR data from 71 lynx predation sites, 53 human hunting sites, 132 locations from 15 GPS-marked roe deer, and 36 roe deer pellet locations from a regional survey, we investigated how predation risk was related to terrain attributes and vegetation classes/structure. As predicted, we found that increasing cover resulted in a contrasting lower predation risk from humans and higher predation risk from lynx. Greater terrain ruggedness increased the predation risk from both predators. Hence, multiple predators may create areas of contrasting risk as well as double risk in the same landscape. Our study highlights the complexity of predatorÃ¯Â¿Â½Ã¯Â¿Â½Ã¯Â¿Â½prey relationship in a multiple predator setting.SynthesisIn this study of risk effects in a multi-predator context, LiDAR data were used to quantify cover in the habitat and relate it to vulnerability to predation in a boreal forest. We found that lynx and human hunters superimpose generally contrasting landscapes of fear on a common prey species, but also identified double-risk zones. Since the benefit of anti-predator responses depends on the combined risk from all predators, it is necessary to consider complete predator assemblages to understand the potential for and occurrence of risk effects across study systems.},
Doi = {10.1111/j.1600-0706.2013.00938.x},
ISSN = {1600-0706},
Owner = {hanso},
Publisher = {Blackwell Publishing Ltd},
Timestamp = {2014.06.30},
Url = {http://dx.doi.org/10.1111/j.1600-0706.2013.00938.x}
}

Design-based estimators for two-stage simple random sampling with regression can have a lack of precision (efficiency) when the primary sampling units (PSUs) vary in size, and PSU totals are approximately proportional to the size of a PSU. Precision and efficiency may deteriorate further for domain-specific estimators when PSUs contain elements from different domains. Design model-unbiased ratio-to-size estimators have been proposed as more efficient. This study introduces a variance estimator for a design model-unbiased ratio estimator. The estimator of variance is derived from a single-stage estimator of a variance of a ratio under two assumptions: the target variable (<i>y</i>) is equal to the sum of a model prediction and two error terms capturing residual errors and model estimation errors; and the existence of an unbiased estimator of model parameters. Extensive simulations confirmed the negative effects of unequal PSU sizes on the precision of the model-assisted estimators of variance and a superior performance of the proposed estimator of variance. These results were confirmed in the analysis of a regional two-stage survey of forest biomass. The proposed variance estimator was generally more efficient than an existing alternative and more stable across a large suite of design settings.

@Article{Magnussen2014,
Title = {An Estimator of Variance for Two-Stage Ratio Regression Estimators},
Author = {Magnussen, Steen and Næsset, Erik and Gobakken, Terje},
Journal = {Forest Science},
Year = {2014},
Number = {4},
Pages = {663-676},
Volume = {60},
Abstract = {Design-based estimators for two-stage simple random sampling with regression can have a lack of precision (efficiency) when the primary sampling units (PSUs) vary in size, and PSU totals are approximately proportional to the size of a PSU. Precision and efficiency may deteriorate further for domain-specific estimators when PSUs contain elements from different domains. Design model-unbiased ratio-to-size estimators have been proposed as more efficient. This study introduces a variance estimator for a design model-unbiased ratio estimator. The estimator of variance is derived from a single-stage estimator of a variance of a ratio under two assumptions: the target variable (<i>y</i>) is equal to the sum of a model prediction and two error terms capturing residual errors and model estimation errors; and the existence of an unbiased estimator of model parameters. Extensive simulations confirmed the negative effects of unequal PSU sizes on the precision of the model-assisted estimators of variance and a superior performance of the proposed estimator of variance. These results were confirmed in the analysis of a regional two-stage survey of forest biomass. The proposed variance estimator was generally more efficient than an existing alternative and more stable across a large suite of design settings.},
Keywords = {auxiliary variables forest inventory large-scale survey ratio-to-size estimator sampling design},
Owner = {hanso},
Timestamp = {2014.10.14}
}

For remote and inaccessible forest regions, lack of sufficient or possibly any sample data inhibits estimation and construction of confidence intervals for population parameters using familiar probability- or design-based inferential methods. Although maps based on remotely sensed data may provide information on the distribution of resources, map-based estimates are subject to classification and prediction error, and map accuracy measures do not directly inform the uncertainty of the estimates. Model-based inference does not require probability samples and when used with synthetic estimation can circumvent small or no-sample difficulties associated with probability-based inference. The study focused on estimating proportion forest area using Landsat data for a study area in Minnesota, USA, and aboveground biomass using airborne laser scanning data for a study area in Hedmark County, Norway. For both study areas, model-based inference was used to estimate the components necessary for constructing confidence intervals for population means for non-sampled areas. The estimates were compared to simple random sampling, model-assisted, and model-based estimates that would have been obtained if the areas had been sampled. All estimates were within two simple random sampling standard errors of each other, thereby illustrating the utility of model-based inference for non-sampled areas.

@Article{McRoberts2014,
Title = {Estimation for inaccessible and non-sampled forest areas using model-based inference and remotely sensed auxiliary information},
Author = {McRoberts, Ronald E. and Næsset, Erik and Gobakken, Terje},
Journal = {Remote Sensing of Environment},
Year = {2014},
Number = {0},
Pages = {226-233},
Volume = {154},
Abstract = {For remote and inaccessible forest regions, lack of sufficient or possibly any sample data inhibits estimation and construction of confidence intervals for population parameters using familiar probability- or design-based inferential methods. Although maps based on remotely sensed data may provide information on the distribution of resources, map-based estimates are subject to classification and prediction error, and map accuracy measures do not directly inform the uncertainty of the estimates. Model-based inference does not require probability samples and when used with synthetic estimation can circumvent small or no-sample difficulties associated with probability-based inference. The study focused on estimating proportion forest area using Landsat data for a study area in Minnesota, USA, and aboveground biomass using airborne laser scanning data for a study area in Hedmark County, Norway. For both study areas, model-based inference was used to estimate the components necessary for constructing confidence intervals for population means for non-sampled areas. The estimates were compared to simple random sampling, model-assisted, and model-based estimates that would have been obtained if the areas had been sampled. All estimates were within two simple random sampling standard errors of each other, thereby illustrating the utility of model-based inference for non-sampled areas.},
Keywords = {Landsat Lidar Precision},
Owner = {hanso},
Timestamp = {2014.10.14}
}

Active learning typically aims at minimizing the number of labeled samples to be included in the training set to reach a certain level of classification accuracy. Standard methods do not usually take into account the real annotation procedures and implicitly assume that all samples require the same effort to be labeled. Here, we consider the case where the cost associated with the annotation of a given sample depends on the previously labeled samples. In general, this is the case when annotating a queried sample is an action that changes the state of a dynamic system, and the cost is a function of the state of the system. In order to minimize the total annotation cost, the active sample selection problem is addressed in the framework of a Markov decision process, which allows one to plan the next labeling action on the basis of an expected long-term cumulative reward. This framework allows us to address the problem of optimizing the collection of labeled samples by field surveys for the classification of remote sensing data. The proposed method is applied to the ground sample collection for tree species classification using airborne hyperspectral images. Experiments carried out in the context of a real case study on forest inventory show the effectiveness of the proposed method.

@Article{Persello2014,
Title = {Cost-Sensitive Active Learning With Lookahead: Optimizing Field Surveys for Remote Sensing Data Classification},
Author = {Persello, C. and Boularias, A. and Dalponte, M. and Gobakken, T. and Næsset, E. and Scholkopf, B.},
Journal = {Geoscience and Remote Sensing, IEEE Transactions on},
Year = {2014},
Number = {99},
Pages = {1-13},
Volume = {PP},
Abstract = {Active learning typically aims at minimizing the number of labeled samples to be included in the training set to reach a certain level of classification accuracy. Standard methods do not usually take into account the real annotation procedures and implicitly assume that all samples require the same effort to be labeled. Here, we consider the case where the cost associated with the annotation of a given sample depends on the previously labeled samples. In general, this is the case when annotating a queried sample is an action that changes the state of a dynamic system, and the cost is a function of the state of the system. In order to minimize the total annotation cost, the active sample selection problem is addressed in the framework of a Markov decision process, which allows one to plan the next labeling action on the basis of an expected long-term cumulative reward. This framework allows us to address the problem of optimizing the collection of labeled samples by field surveys for the classification of remote sensing data. The proposed method is applied to the ground sample collection for tree species classification using airborne hyperspectral images. Experiments carried out in the context of a real case study on forest inventory show the effectiveness of the proposed method.},
Keywords = {Accuracy Hyperspectral imaging Labeling Support vector machines Training Uncertainty Active learning (AL) Markov decision process (MDP) field surveys forest inventories hyperspectral data image classification support vector machine (SVM)},
Owner = {hanso},
Timestamp = {2014.10.14}
}

BACKGROUND:There is a need for new satellite remote sensing methods for monitoring tropical forest carbon stocks. Advanced RADAR instruments on board satellites can contribute with novel methods. RADARs can see through clouds, and furthermore, by applying stereo RADAR imaging we can measure forest height and its changes. Such height changes are related to carbon stock changes in the biomass. We here apply data from the current Tandem-X satellite mission, where two RADAR equipped satellites go in close formation providing stereo imaging. We combine that with similar data acquired with one of the space shuttles in the year 2000, i.e. the so-called SRTM mission. We derive height information from a RADAR image pair using a method called interferometry.RESULTS:We demonstrate an approach for REDD based on interferometry data from a boreal forest in Norway. We fitted a model to the data where above-ground biomass in the forest increases with 15t/ha for every m increase of the height of the RADAR echo. When the RADAR echo is at the ground the estimated biomass is zero, and when it is 20m above the ground the estimated above-ground biomass is 300t/ha. Using this model we obtained fairly accurate estimates of biomass changes from 2000 to 2011. For 200m2 plots we obtained an accuracy of 65t/ha, which corresponds to 50% of the mean above-ground biomass value. We also demonstrate that this method can be applied without having accurate terrain heights and without having former in-situ biomass data, both of which are generally lacking in tropical countries. The gain in accuracy was marginal when we included such data in the estimation. Finally, we demonstrate that logging and other biomass changes can be accurately mapped. A biomass change map based on interferometry corresponded well to a very accurate map derived from repeated scanning with airborne laser.CONCLUSIONS:Satellite based, stereo imaging with advanced RADAR instruments appears to be a promising method for REDD. Interferometric processing of the RADAR data provides maps of forest height changes from which we can estimate temporal changes in biomass and carbon.

@Article{Solberg2014,
Title = {Forest biomass change estimated from height change in interferometric SAR height models},
Author = {Solberg, Svein and Næsset, Erik and Gobakken, Terje and Bollandsås, Ole-Martin},
Journal = {Carbon Balance and Management},
Year = {2014},
Number = {1},
Pages = {5},
Volume = {9},
Abstract = {BACKGROUND:There is a need for new satellite remote sensing methods for monitoring tropical forest carbon stocks. Advanced RADAR instruments on board satellites can contribute with novel methods. RADARs can see through clouds, and furthermore, by applying stereo RADAR imaging we can measure forest height and its changes. Such height changes are related to carbon stock changes in the biomass. We here apply data from the current Tandem-X satellite mission, where two RADAR equipped satellites go in close formation providing stereo imaging. We combine that with similar data acquired with one of the space shuttles in the year 2000, i.e. the so-called SRTM mission. We derive height information from a RADAR image pair using a method called interferometry.RESULTS:We demonstrate an approach for REDD based on interferometry data from a boreal forest in Norway. We fitted a model to the data where above-ground biomass in the forest increases with 15t/ha for every m increase of the height of the RADAR echo. When the RADAR echo is at the ground the estimated biomass is zero, and when it is 20m above the ground the estimated above-ground biomass is 300t/ha. Using this model we obtained fairly accurate estimates of biomass changes from 2000 to 2011. For 200m2 plots we obtained an accuracy of 65t/ha, which corresponds to 50% of the mean above-ground biomass value. We also demonstrate that this method can be applied without having accurate terrain heights and without having former in-situ biomass data, both of which are generally lacking in tropical countries. The gain in accuracy was marginal when we included such data in the estimation. Finally, we demonstrate that logging and other biomass changes can be accurately mapped. A biomass change map based on interferometry corresponded well to a very accurate map derived from repeated scanning with airborne laser.CONCLUSIONS:Satellite based, stereo imaging with advanced RADAR instruments appears to be a promising method for REDD. Interferometric processing of the RADAR data provides maps of forest height changes from which we can estimate temporal changes in biomass and carbon.},
Owner = {hanso},
Timestamp = {2014.10.14}
}

A computational canopy volume (CCV) based on airborne laser scanning (ALS) data is proposed to improve predictions of forest biomass and other related attributes like stem volume and basal area. An approach to derive the CCV based on computational geometry, topological connectivity and numerical optimization was tested with sparse-density, plot-level ALS data acquired from 40 field sample plots of 500Ã¢â‚¬â€œ1000 m2 located in a boreal forest in Norway. The CCV had a high correspondence with the biomass attributes considered when derived from optimized filtrations, i.e. ordered sets of simplices belonging to the triangulations based on the point data. Coefficients of determination (R2) between the CCV and total above-ground biomass, canopy biomass, stem volume, and basal area were 0.88Ã¢â‚¬â€œ0.89, 0.89, 0.83Ã¢â‚¬â€œ0.97, and 0.88Ã¢â‚¬â€œ0.92, respectively, depending on the applied filtration. The magnitude of the required filtration was found to increase according to an increasing basal area, which indicated a possibility to predict this magnitude by means of ALS-based height and density metrics. A simple prediction model provided CCVs which had R2 of 0.77Ã¢â‚¬â€œ0.90 with the aforementioned forest attributes. The derived CCVs always produced complementary information and were mainly able to improve the predictions of forest biomass relative to models based on the height and density metrics, yet only by 0Ã¢â‚¬â€œ1.9 percentage points in terms of relative root mean squared error. Possibilities to improve the CCVs by a further analysis of topological persistence are discussed.

@Article{Vauhkonen2014,
Title = {Deriving airborne laser scanning based computational canopy volume for forest biomass and allometry studies},
Author = {Vauhkonen, Jari and Næsset, Erik and Gobakken, Terje},
Journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
Year = {2014},
Number = {0},
Pages = {57-66},
Volume = {96},
Abstract = {A computational canopy volume (CCV) based on airborne laser scanning (ALS) data is proposed to improve predictions of forest biomass and other related attributes like stem volume and basal area. An approach to derive the CCV based on computational geometry, topological connectivity and numerical optimization was tested with sparse-density, plot-level ALS data acquired from 40 field sample plots of 500Ã¢â‚¬â€œ1000 m2 located in a boreal forest in Norway. The CCV had a high correspondence with the biomass attributes considered when derived from optimized filtrations, i.e. ordered sets of simplices belonging to the triangulations based on the point data. Coefficients of determination (R2) between the CCV and total above-ground biomass, canopy biomass, stem volume, and basal area were 0.88Ã¢â‚¬â€œ0.89, 0.89, 0.83Ã¢â‚¬â€œ0.97, and 0.88Ã¢â‚¬â€œ0.92, respectively, depending on the applied filtration. The magnitude of the required filtration was found to increase according to an increasing basal area, which indicated a possibility to predict this magnitude by means of ALS-based height and density metrics. A simple prediction model provided CCVs which had R2 of 0.77Ã¢â‚¬â€œ0.90 with the aforementioned forest attributes. The derived CCVs always produced complementary information and were mainly able to improve the predictions of forest biomass relative to models based on the height and density metrics, yet only by 0Ã¢â‚¬â€œ1.9 percentage points in terms of relative root mean squared error. Possibilities to improve the CCVs by a further analysis of topological persistence are discussed.},
Keywords = {Light Detection and Ranging (LiDAR) Forest inventory Tree allometry Delaunay triangulation Alpha shape Simplicial homomorphism Persistent homology},
Owner = {hanso},
Timestamp = {2014.10.14}
}

This study used two different approaches to model diameter distributions on data from 201 field plots in a boreal conifer forest in south eastern Norway using airborne laser scanning. These two methods were a non-parametric most similar neighbour (MSN) approach and a parametric seemingly unrelated regression (SUR) approach to predict diameter percentiles, and their accuracies were compared by validation with an independent dataset. Based on calculated differences between predicted and observed number of stems on the entire validation dataset, we found that SUR gave unbiased results and that MSN slightly underestimated total number of stems. However, both methods overpredicted the number of stems per hectare in the range of 15.6Ã¢â‚¬â€œ61.5 stems in the smallest diameter classes (between 4 and 12 cm). If the predicted diameter distributions were converted into basal area per hectare (G), both methods gave unbiased results. The average difference for G was 1.9 per cent of the observed value for the MSN approach. The corresponding number for the SUR model was 12.4 per cent. Neither of these differences were statistically significant (P > 0.05). We concluded that the even though both methods overall yielded accurate results, the MSN approach was more reliable in terms of predicting the number of large trees.

@Article{BollandsA¥s2013a,
Title = {Comparing parametric and non-parametric modelling of diameter distributions on independent data using airborne laser scanning in a boreal conifer forest},
Author = {Bollandsås, Ole Martin and Maltamo, Matti and Gobakken, Terje and Næsset, Erik},
Journal = {Forestry},
Year = {2013},
Number = {4},
Pages = {493-501},
Volume = {86},
Abstract = {This study used two different approaches to model diameter distributions on data from 201 field plots in a boreal conifer forest in south eastern Norway using airborne laser scanning. These two methods were a non-parametric most similar neighbour (MSN) approach and a parametric seemingly unrelated regression (SUR) approach to predict diameter percentiles, and their accuracies were compared by validation with an independent dataset. Based on calculated differences between predicted and observed number of stems on the entire validation dataset, we found that SUR gave unbiased results and that MSN slightly underestimated total number of stems. However, both methods overpredicted the number of stems per hectare in the range of 15.6Ã¢â‚¬â€œ61.5 stems in the smallest diameter classes (between 4 and 12 cm). If the predicted diameter distributions were converted into basal area per hectare (G), both methods gave unbiased results. The average difference for G was 1.9 per cent of the observed value for the MSN approach. The corresponding number for the SUR model was 12.4 per cent. Neither of these differences were statistically significant (P > 0.05). We concluded that the even though both methods overall yielded accurate results, the MSN approach was more reliable in terms of predicting the number of large trees.},
Doi = {10.1093/forestry/cpt020},
Eprint = {http://forestry.oxfordjournals.org/content/86/4/493.full.pdf+html},
Owner = {hanso},
Timestamp = {2014.10.15},
Url = {http://forestry.oxfordjournals.org/content/86/4/493.abstract}
}

In this study we introduced a novel unsupervised selection method for collecting training samples for tree species classification at individual tree crown (ITC) level using hyperspectral data. The selection process is based on a search strategy and a distance metric defined among the percentiles derived from the spectral distributions of the pixels inside the ITCs. The method was developed using two kinds of samples: i) plots, and ii) ITCs. The experimental results indicated that the method allows reducing the amount of training samples needed for the classification process, without significantly decreasing the classification accuracy.

The <I>k</I>-near neighbours (<I>k</I>-NN) technique combines field data from forest inventories and auxiliary information for forest resource estimation at various geographical scales. In this study, auxiliary data consisting of Landsat 5 TM satellite imagery and terrain elevations were used to perform <I>k</I>-NN imputations of plot-level above ground biomass. Following the model-based inference, a superpopulation model consisting of a canonical vine copula was constructed from the empirical data, and new samples were generated from the model and used for <I>k</I>-NN predictions. The method used herein allows constructing the sampling distribution for the imputation errors and for assessing the statistical properties of the <I>k</I>-NN estimator. Using a data-splitting procedure, the copula-based approach was assessed against pair-bootstrap resampling. The imputations were performed using <I>k</I> (the number of neighbours) = 1 and by using optimal <I>k</I> values selected according to a bias-minimizing criterion. The empirical coverage probabilities of the confidence intervals constructed using the copula-based approach were closer to the nominal coverages. The improvements were due to significant bias reduction, while the standard errors were higher compared to the bootstrap. Still, the root mean squared error was significantly reduced. The best results were obtained using the copula approach and <I>k</I>-NN imputations with <I>k</I>=1.

@Article{Ene2013,
Title = {Model-based inference for k-nearest neighbours predictions using a canonical vine copula},
Author = {Ene, Liviu Theodor and Næsset, Erik and Gobakken, Terje},
Journal = {Scandinavian Journal of Forest Research},
Year = {2013},
Number = {3},
Pages = {266-281},
Volume = {28},
Abstract = {The <I>k</I>-near neighbours (<I>k</I>-NN) technique combines field data from forest inventories and auxiliary information for forest resource estimation at various geographical scales. In this study, auxiliary data consisting of Landsat 5 TM satellite imagery and terrain elevations were used to perform <I>k</I>-NN imputations of plot-level above ground biomass. Following the model-based inference, a superpopulation model consisting of a canonical vine copula was constructed from the empirical data, and new samples were generated from the model and used for <I>k</I>-NN predictions. The method used herein allows constructing the sampling distribution for the imputation errors and for assessing the statistical properties of the <I>k</I>-NN estimator. Using a data-splitting procedure, the copula-based approach was assessed against pair-bootstrap resampling. The imputations were performed using <I>k</I> (the number of neighbours) = 1 and by using optimal <I>k</I> values selected according to a bias-minimizing criterion. The empirical coverage probabilities of the confidence intervals constructed using the copula-based approach were closer to the nominal coverages. The improvements were due to significant bias reduction, while the standard errors were higher compared to the bootstrap. Still, the root mean squared error was significantly reduced. The best results were obtained using the copula approach and <I>k</I>-NN imputations with <I>k</I>=1.},
Doi = {10.1080/02827581.2012.723743},
Keywords = {copulas k-NN imputations variance estimation},
Owner = {hanso},
Timestamp = {2014.10.15},
Url = {http://dx.doi.org/10.1080/02827581.2012.723743}
}

Field measurements conducted on sample plots are a major cost component in airborne laser scanning (ALS) based forest inventories, as field data is needed to obtain reference variables for the statistical models. The ALS data also provides an excellent source of prior information that may be used in the design phase of the field survey to reduce the size of the field data set. In the current study, we acquired two independent modeling data sets: one with ALS-assisted and another with random plot selection. A third data set was used for validation. One canopy height and one canopy density variable were used as a basis for the ALS-assisted selection. Ordinary and partial least squares regressions for stem volume were fitted for four different strata using the two data sets separately. The results show that the ALS-assisted plot selection helped to decrease the root mean square error (RMSE) of the predicted volume. Although the differences in RMSE were relatively small, models based on random plot selection showed larger mean differences from the reference in the independent validation data. Furthermore, a sub-sampling experiment showed that 40 well placed plots should be enough for fairly reliable predictions.

@Article{Gobakken2013,
Title = {Laser-assisted selection of field plots for an area-based forest inventory},
Author = {Gobakken, Terje and Korhonen, Lauri and Næsset, Erik},
Journal = {SILVA FENNICA},
Year = {2013},
Number = {5},
Pages = {1-20},
Volume = {47},
Abstract = {Field measurements conducted on sample plots are a major cost component in airborne laser scanning (ALS) based forest inventories, as field data is needed to obtain reference variables for the statistical models. The ALS data also provides an excellent source of prior information that may be used in the design phase of the field survey to reduce the size of the field data set. In the current study, we acquired two independent modeling data sets: one with ALS-assisted and another with random plot selection. A third data set was used for validation. One canopy height and one canopy density variable were used as a basis for the ALS-assisted selection. Ordinary and partial least squares regressions for stem volume were fitted for four different strata using the two data sets separately. The results show that the ALS-assisted plot selection helped to decrease the root mean square error (RMSE) of the predicted volume. Although the differences in RMSE were relatively small, models based on random plot selection showed larger mean differences from the reference in the independent validation data. Furthermore, a sub-sampling experiment showed that 40 well placed plots should be enough for fairly reliable predictions.},
Keywords = { airborne laser scanning; lidar; area-based approach; forest inventory; stratified sampling},
Owner = {hanso},
Timestamp = {2014.10.15}
}

Many remote sensing-based methods estimating forest biomass rely on allometric biomass models for field reference data. Terrestrial laser scanning (TLS) has emerged as a tool for detailed data collection in forestry applications, and the methods have been proposed to derive, e.g. tree position, diameter-at-breast-height, and stem volume from TLS data. In this study, TLS-derived features were related to destructively sampled branch biomass of Norway spruce at the single-tree level, and the results were compared to conventional allometric models with field measured diameter and height. TLS features were derived following two approaches: one voxel-based approach with a detailed analysis of the interaction between individual voxels and each laser beam. The features were derived using voxels of size 0.1, 0.2, and 0.4 m, and the effect of the voxel size was assessed. The voxel-derived features were compared to features derived from crown dimension measurements in the unified TLS point cloud data. TLS-derived variables were used in regression models, and prediction accuracies were assessed through a Monte Carlo cross-validation procedure. The model based on 0.4 m voxel data yielded the best prediction accuracy, with a root mean square error (RMSE) of 32%. The accuracy was found to decrease with an increase in voxel size, i.e. the model based on the 0.1 m voxel yielded the lowest accuracy. The model based on crown measurements had an RMSE of 34%. The accuracies of the predictions from the TLS-based models were found to be higher than from conventional allometric models, but the improvement was relatively small.

@Article{Hauglin2013,
Title = {Estimating single-tree branch biomass of Norway spruce with terrestrial laser scanning using voxel-based and crown dimension features},
Author = {Hauglin, Marius and Astrup, Rasmus and Gobakken, Terje and Næsset, Erik},
Journal = {Scandinavian Journal of Forest Research},
Year = {2013},
Number = {5},
Pages = {456-469},
Volume = {28},
Abstract = {Many remote sensing-based methods estimating forest biomass rely on allometric biomass models for field reference data. Terrestrial laser scanning (TLS) has emerged as a tool for detailed data collection in forestry applications, and the methods have been proposed to derive, e.g. tree position, diameter-at-breast-height, and stem volume from TLS data. In this study, TLS-derived features were related to destructively sampled branch biomass of Norway spruce at the single-tree level, and the results were compared to conventional allometric models with field measured diameter and height. TLS features were derived following two approaches: one voxel-based approach with a detailed analysis of the interaction between individual voxels and each laser beam. The features were derived using voxels of size 0.1, 0.2, and 0.4 m, and the effect of the voxel size was assessed. The voxel-derived features were compared to features derived from crown dimension measurements in the unified TLS point cloud data. TLS-derived variables were used in regression models, and prediction accuracies were assessed through a Monte Carlo cross-validation procedure. The model based on 0.4 m voxel data yielded the best prediction accuracy, with a root mean square error (RMSE) of 32%. The accuracy was found to decrease with an increase in voxel size, i.e. the model based on the 0.1 m voxel yielded the lowest accuracy. The model based on crown measurements had an RMSE of 34%. The accuracies of the predictions from the TLS-based models were found to be higher than from conventional allometric models, but the improvement was relatively small.},
Doi = {10.1080/02827581.2013.777772},
Keywords = {terrestrial laser scanning biomass forest inventory lidar},
Owner = {hanso},
Timestamp = {2014.10.15},
Url = {http://dx.doi.org/10.1080/02827581.2013.777772}
}

The use of forest biomass for bioenergy purposes, directly or through refinement processes, has increased in the last decade. One example of such use is the utilization of logging residues. Branch biomass constitutes typically a considerable part of the logging residues, and should be quantified and included in future forest inventories. Airborne laser scanning (ALS) is widely used when collecting data for forest inventories, and even methods to derive information at the single-tree level has been described. Procedures for estimation of single-tree branch biomass of Norway spruce using features derived from ALS data are proposed in the present study. As field reference data the dry weight branch biomass of 50 trees were obtained through destructive sampling. Variables were further derived from the ALS echoes from each tree, including crown volume calculated from an interpolated crown surface constructed with a radial basis function. Spatial information derived from the pulse vectors were also incorporated when calculating the crown volume. Regression models with branch biomass as response variable were fit to the data, and the prediction accuracy assessed through a cross-validation procedure. Random forest regression models were compared to stepwise and simple linear least squares models. In the present study branch biomass was estimated with a higher accuracy by the best ALS-based models than by existing allometric biomass equations based on field measurements. An improved prediction accuracy was observed when incorporating information from the laser pulse vectors into the calculation of the crown volume variable, and a linear model with the crown volume as a single predictor gave the best overall results with a root mean square error of 35% in the validation.

@Article{Hauglin2013a,
Title = {Estimating single-tree branch biomass of Norway spruce by airborne laser scanning},
Author = {Hauglin, Marius and Dibdiakova, Janka and Gobakken, Terje and Næsset, Erik},
Journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
Year = {2013},
Number = {0},
Pages = {147-156},
Volume = {79},
Abstract = {The use of forest biomass for bioenergy purposes, directly or through refinement processes, has increased in the last decade. One example of such use is the utilization of logging residues. Branch biomass constitutes typically a considerable part of the logging residues, and should be quantified and included in future forest inventories. Airborne laser scanning (ALS) is widely used when collecting data for forest inventories, and even methods to derive information at the single-tree level has been described. Procedures for estimation of single-tree branch biomass of Norway spruce using features derived from ALS data are proposed in the present study. As field reference data the dry weight branch biomass of 50 trees were obtained through destructive sampling. Variables were further derived from the ALS echoes from each tree, including crown volume calculated from an interpolated crown surface constructed with a radial basis function. Spatial information derived from the pulse vectors were also incorporated when calculating the crown volume. Regression models with branch biomass as response variable were fit to the data, and the prediction accuracy assessed through a cross-validation procedure. Random forest regression models were compared to stepwise and simple linear least squares models. In the present study branch biomass was estimated with a higher accuracy by the best ALS-based models than by existing allometric biomass equations based on field measurements. An improved prediction accuracy was observed when incorporating information from the laser pulse vectors into the calculation of the crown volume variable, and a linear model with the crown volume as a single predictor gave the best overall results with a root mean square error of 35% in the validation.},
Doi = {10.1016/j.isprsjprs.2013.02.013},
Keywords = {Forestry LIDAR Inventory Estimation Accuracy},
Owner = {hanso},
Timestamp = {2014.10.15},
Url = {http://dx.doi.org/10.1016/j.isprsjprs.2013.02.013}
}

The method of predicting an unknown target probability distribution via a GramÃ¢â‚¬â€œCharlier A-series expansion (GCAE) of a user-defined base probability function and cumulants of a known distribution of an auxiliary variable is demonstrated in two applications. Both applications concern predictions of the distribution of tree stem diameters with cumulants of airborne laser scanning (ALS) canopy heights and an index of canopy density as predictors. All predictions were generated in a leave-one-out cross-validation scheme, and statistical inference was based on 100 stochastic predictions of the tree sizes in 308 plots of 400 m2. The mean and variance of GCAE-predicted distributions were rarely significantly different from actual values, yet between 19 and 32% of the predicted GCAE distributions were significantly different from the actual distribution. The rejection rate with predictions generated from a simpler DECILE method was, on average, 2.5% lower. GCAE is still recommended due to its potential usefulness. Cumulants of ALS canopy heights are independent of plot area and effective for area-based least-squares predictions of forest inventory variables.

@Article{Magnussen2013,
Title = {Prediction of tree-size distributions and inventory variables from cumulants of canopy height distributions},
Author = {Magnussen, Steen and Næsset, Erik and Gobakken, Terje},
Journal = {Forestry},
Year = {2013},
Number = {5},
Pages = {583-595},
Volume = {86},
Abstract = {The method of predicting an unknown target probability distribution via a GramÃ¢â‚¬â€œCharlier A-series expansion (GCAE) of a user-defined base probability function and cumulants of a known distribution of an auxiliary variable is demonstrated in two applications. Both applications concern predictions of the distribution of tree stem diameters with cumulants of airborne laser scanning (ALS) canopy heights and an index of canopy density as predictors. All predictions were generated in a leave-one-out cross-validation scheme, and statistical inference was based on 100 stochastic predictions of the tree sizes in 308 plots of 400 m2. The mean and variance of GCAE-predicted distributions were rarely significantly different from actual values, yet between 19 and 32% of the predicted GCAE distributions were significantly different from the actual distribution. The rejection rate with predictions generated from a simpler DECILE method was, on average, 2.5% lower. GCAE is still recommended due to its potential usefulness. Cumulants of ALS canopy heights are independent of plot area and effective for area-based least-squares predictions of forest inventory variables.},
Owner = {hanso},
Timestamp = {2014.10.15}
}

In a forest inventory context, estimation for small areas and for remote and inaccessible regions may be problematic using traditional probability- or design-based inference because acquisition of sufficiently large samples to satisfy precision requirements is financially and/or logistically difficult. These problems can often be partially alleviated for inventory applications by enhancing inferences using models and remotely sensed independent variables. However, estimates obtained using probability-based, model-assisted estimators may still suffer detrimental effects as the result of small sample sizes. Model-based inference has the potential to alleviate these problems because precision is affected by other factors such as model specification. Nevertheless, model specification in the form of selection of independent variables often focuses exclusively on quality of fit with little consideration given to the precision of estimates of areal population parameters. Model-based inference is illustrated for two forest inventory applications, estimation of mean proportion forest area using Landsat-based independent variables for a study area in the USA and estimation of mean growing stock volume per unit area using lidar-based independent variables for a study area in Norway. Variations of a nonlinear logistic regression model are used for both applications. The results indicate selection of subsets of remotely sensed independent variables to maximize precision had negligible effects on the quality of fit of the models to the data and on estimates of means but substantial proportional beneficial effects on precision.

@Article{McRoberts2013a,
Title = {Accuracy and Precision for Remote Sensing Applications of Nonlinear Model-Based Inference},
Author = {McRoberts, R. E. and Naesset, E. and Gobakken, T.},
Journal = {Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
Year = {2013},
Number = {1},
Pages = {27-34},
Volume = {6},
Abstract = {In a forest inventory context, estimation for small areas and for remote and inaccessible regions may be problematic using traditional probability- or design-based inference because acquisition of sufficiently large samples to satisfy precision requirements is financially and/or logistically difficult. These problems can often be partially alleviated for inventory applications by enhancing inferences using models and remotely sensed independent variables. However, estimates obtained using probability-based, model-assisted estimators may still suffer detrimental effects as the result of small sample sizes. Model-based inference has the potential to alleviate these problems because precision is affected by other factors such as model specification. Nevertheless, model specification in the form of selection of independent variables often focuses exclusively on quality of fit with little consideration given to the precision of estimates of areal population parameters. Model-based inference is illustrated for two forest inventory applications, estimation of mean proportion forest area using Landsat-based independent variables for a study area in the USA and estimation of mean growing stock volume per unit area using lidar-based independent variables for a study area in Norway. Variations of a nonlinear logistic regression model are used for both applications. The results indicate selection of subsets of remotely sensed independent variables to maximize precision had negligible effects on the quality of fit of the models to the data and on estimates of means but substantial proportional beneficial effects on precision.},
Keywords = {Biological system modeling Data models Logistics Mathematical model Remote sensing Sociology Statistics Landsat lidar variable selection},
Owner = {hanso},
Timestamp = {2014.10.15}
}

In a forest inventory context, estimation for small areas and for remote and inaccessible regions may be problematic using traditional probability- or design-based inference because acquisition of sufficiently large samples to satisfy precision requirements is financially and/or logistically difficult. These problems can often be partially alleviated for inventory applications by enhancing inferences using models and remotely sensed independent variables. However, estimates obtained using probability-based, model-assisted estimators may still suffer detrimental effects as the result of small sample sizes. Model-based inference has the potential to alleviate these problems because precision is affected by other factors such as model specification. Nevertheless, model specification in the form of selection of independent variables often focuses exclusively on quality of fit with little consideration given to the precision of estimates of areal population parameters. Model-based inference is illustrated for two forest inventory applications, estimation of mean proportion forest area using Landsat-based independent variables for a study area in the USA and estimation of mean growing stock volume per unit area using lidar-based independent variables for a study area in Norway. Variations of a nonlinear logistic regression model are used for both applications. The results indicate selection of subsets of remotely sensed independent variables to maximize precision had negligible effects on the quality of fit of the models to the data and on estimates of means but substantial proportional beneficial effects on precision.

@Article{McRoberts2013b,
Title = {Accuracy and Precision for Remote Sensing Applications of Nonlinear Model-Based Inference},
Author = {McRoberts, R. E. and Næsset, E. and Gobakken, T.},
Journal = {Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
Year = {2013},
Number = {1},
Pages = {27-34},
Volume = {6},
Abstract = {In a forest inventory context, estimation for small areas and for remote and inaccessible regions may be problematic using traditional probability- or design-based inference because acquisition of sufficiently large samples to satisfy precision requirements is financially and/or logistically difficult. These problems can often be partially alleviated for inventory applications by enhancing inferences using models and remotely sensed independent variables. However, estimates obtained using probability-based, model-assisted estimators may still suffer detrimental effects as the result of small sample sizes. Model-based inference has the potential to alleviate these problems because precision is affected by other factors such as model specification. Nevertheless, model specification in the form of selection of independent variables often focuses exclusively on quality of fit with little consideration given to the precision of estimates of areal population parameters. Model-based inference is illustrated for two forest inventory applications, estimation of mean proportion forest area using Landsat-based independent variables for a study area in the USA and estimation of mean growing stock volume per unit area using lidar-based independent variables for a study area in Norway. Variations of a nonlinear logistic regression model are used for both applications. The results indicate selection of subsets of remotely sensed independent variables to maximize precision had negligible effects on the quality of fit of the models to the data and on estimates of means but substantial proportional beneficial effects on precision.},
Doi = {10.1109/JSTARS.2012.2227299},
Keywords = {Biological system modeling Data models Logistics Mathematical model Remote sensing Sociology Statistics Landsat lidar variable selection},
Owner = {hanso},
Timestamp = {2014.10.15},
Url = {http://dx.doi.org/10.1109/JSTARS.2012.2227299}
}

Abstract Miombo woodland is a significant forest type occupying about 9% of the African land area and forms a dominant vegetation type in many southeastern African countries including Tanzania. Quantification of the amount of carbon stored in forests presently is an important component in the implementation of the emerging carbon credit market mechanisms. This calls for appropriate allometric models predicting biomass which currently are scarce. The aim of this study was to develop above- and belowground allometric general and site-specific models for trees in miombo woodland. The data were collected from four sites in Tanzania and covers a wide range of conditions and tree sizes (diameters at breast height from 1.1 to 110&#xa0;cm). Above- and belowground biomass models were developed from 167 and 80 sample trees, respectively. The model fitting showed that large parts of the variation (up to 97%) in biomass were explained by diameter at breast height and tree height. Since including tree height only marginally increased the explanation of the biomass variation (from 95% to 96Ã¢â‚¬â€œ97% for aboveground biomass), the general recommendation is to apply the models with diameter at breast height only as an independent variable. The results also showed that the general models can be applied over a wide range of conditions in Tanzania. The comparison with previously developed models revealed that these models can probably also be applied for miombo woodland elsewhere in southeastern Africa if not used beyond the tree size range of the model data.

@Article{Mugasha2013,
Title = {Allometric models for prediction of above- and belowground biomass of trees in the miombo woodlands of Tanzania },
Author = {Wilson Ancelm Mugasha and Tron Eid and Ole Martin Bollandsås and Rogers Ernest Malimbwi and Shabani Athumani Omari Chamshama and Eliakimu Zahabu and Josiah Zephania Katani},
Journal = {Forest Ecology and Management },
Year = {2013},
Number = {0},
Pages = {87 - 101},
Volume = {310},
Abstract = {Abstract Miombo woodland is a significant forest type occupying about 9% of the African land area and forms a dominant vegetation type in many southeastern African countries including Tanzania. Quantification of the amount of carbon stored in forests presently is an important component in the implementation of the emerging carbon credit market mechanisms. This calls for appropriate allometric models predicting biomass which currently are scarce. The aim of this study was to develop above- and belowground allometric general and site-specific models for trees in miombo woodland. The data were collected from four sites in Tanzania and covers a wide range of conditions and tree sizes (diameters at breast height from 1.1 to 110&#xa0;cm). Above- and belowground biomass models were developed from 167 and 80 sample trees, respectively. The model fitting showed that large parts of the variation (up to 97%) in biomass were explained by diameter at breast height and tree height. Since including tree height only marginally increased the explanation of the biomass variation (from 95% to 96Ã¢â‚¬â€œ97% for aboveground biomass), the general recommendation is to apply the models with diameter at breast height only as an independent variable. The results also showed that the general models can be applied over a wide range of conditions in Tanzania. The comparison with previously developed models revealed that these models can probably also be applied for miombo woodland elsewhere in southeastern Africa if not used beyond the tree size range of the model data.},
Doi = {http://dx.doi.org/10.1016/j.foreco.2013.08.003},
ISSN = {0378-1127},
Keywords = {<!-- Tag Not Handled --><keyword id=#k0030#>Destructive sampling},
Owner = {hanso},
Timestamp = {2014.10.15},
Url = {http://www.sciencedirect.com/science/article/pii/S0378112713005306}
}

Airborne scanning LiDAR (Light Detection and Ranging) has emerged as a promising tool to provide auxiliary data for sample surveys aiming at estimation of above-ground tree biomass (AGB), with potential applications in REDD forest monitoring. For larger geographical regions such as counties, states or nations, it is not feasible to collect airborne LiDAR data continuously (Ã¢â‚¬Å“wall-to-wallÃ¢â‚¬ï¿½) over the entire area of interest. Two-stage cluster survey designs have therefore been demonstrated by which LiDAR data are collected along selected individual flight-lines treated as clusters and with ground plots sampled along these LiDAR swaths. Recently, analytical AGB estimators and associated variance estimators that quantify the sampling variability have been proposed. Empirical studies employing these estimators have shown a seemingly equal or even larger uncertainty of the AGB estimates obtained with extensive use of LiDAR data to support the estimation as compared to pure field-based estimates employing estimators appropriate under simple random sampling (SRS). However, comparison of uncertainty estimates under SRS and sophisticated two-stage designs is complicated by large differences in the designs and assumptions. In this study, probability-based principles to estimation and inference were followed. We assumed designs of a field sample and a LiDAR-assisted survey of Hedmark County (HC) (27,390 km2), Norway, considered to be more comparable than those assumed in previous studies. The field sample consisted of 659 systematically distributed National Forest Inventory (NFI) plots and the airborne scanning LiDAR data were collected along 53 parallel flight-lines flown over the NFI plots. We compared AGB estimates based on the field survey only assuming SRS against corresponding estimates assuming two-phase (double) sampling with LiDAR and employing model-assisted estimators. We also compared AGB estimates based on the field survey only assuming two-stage sampling (the NFI plots being grouped in clusters) against corresponding estimates assuming two-stage sampling with the LiDAR and employing model-assisted estimators. For each of the two comparisons, the standard errors of the AGB estimates were consistently lower for the LiDAR-assisted designs. The overall reduction of the standard errors in the LiDAR-assisted estimation was around 40Ã¢â‚¬â€œ60% compared to the pure field survey. We conclude that the previously proposed two-stage model-assisted estimators are inappropriate for surveys with unequal lengths of the LiDAR flight-lines and new estimators are needed. Some options for design of LiDAR-assisted sample surveys under REDD are also discussed, which capitalize on the flexibility offered when the field survey is designed as an integrated part of the overall survey design as opposed to previous LiDAR-assisted sample surveys in the boreal and temperate zones which have been restricted by the current design of an existing NFI.

@Article{NA¦sset2013a,
Title = {Comparison of precision of biomass estimates in regional field sample surveys and airborne LiDAR-assisted surveys in Hedmark County, Norway},
Author = {Næsset, Erik and Gobakken, Terje and Bollandsås, Ole Martin and Gregoire, Timothy G. and Nelson, Ross and Ståhl, GÃ¶an},
Journal = {Remote Sensing of Environment},
Year = {2013},
Number = {0},
Pages = {108-120},
Volume = {130},
Abstract = {Airborne scanning LiDAR (Light Detection and Ranging) has emerged as a promising tool to provide auxiliary data for sample surveys aiming at estimation of above-ground tree biomass (AGB), with potential applications in REDD forest monitoring. For larger geographical regions such as counties, states or nations, it is not feasible to collect airborne LiDAR data continuously (Ã¢â‚¬Å“wall-to-wallÃ¢â‚¬ï¿½) over the entire area of interest. Two-stage cluster survey designs have therefore been demonstrated by which LiDAR data are collected along selected individual flight-lines treated as clusters and with ground plots sampled along these LiDAR swaths. Recently, analytical AGB estimators and associated variance estimators that quantify the sampling variability have been proposed. Empirical studies employing these estimators have shown a seemingly equal or even larger uncertainty of the AGB estimates obtained with extensive use of LiDAR data to support the estimation as compared to pure field-based estimates employing estimators appropriate under simple random sampling (SRS). However, comparison of uncertainty estimates under SRS and sophisticated two-stage designs is complicated by large differences in the designs and assumptions. In this study, probability-based principles to estimation and inference were followed. We assumed designs of a field sample and a LiDAR-assisted survey of Hedmark County (HC) (27,390 km2), Norway, considered to be more comparable than those assumed in previous studies. The field sample consisted of 659 systematically distributed National Forest Inventory (NFI) plots and the airborne scanning LiDAR data were collected along 53 parallel flight-lines flown over the NFI plots. We compared AGB estimates based on the field survey only assuming SRS against corresponding estimates assuming two-phase (double) sampling with LiDAR and employing model-assisted estimators. We also compared AGB estimates based on the field survey only assuming two-stage sampling (the NFI plots being grouped in clusters) against corresponding estimates assuming two-stage sampling with the LiDAR and employing model-assisted estimators. For each of the two comparisons, the standard errors of the AGB estimates were consistently lower for the LiDAR-assisted designs. The overall reduction of the standard errors in the LiDAR-assisted estimation was around 40Ã¢â‚¬â€œ60% compared to the pure field survey. We conclude that the previously proposed two-stage model-assisted estimators are inappropriate for surveys with unequal lengths of the LiDAR flight-lines and new estimators are needed. Some options for design of LiDAR-assisted sample surveys under REDD are also discussed, which capitalize on the flexibility offered when the field survey is designed as an integrated part of the overall survey design as opposed to previous LiDAR-assisted sample surveys in the boreal and temperate zones which have been restricted by the current design of an existing NFI.},
Doi = {10.1016/j.rse.2012.11.010},
Keywords = {Forest monitoring Airborne LiDAR Probability sampling Biomass estimation Model-assisted estimation},
Owner = {hanso},
Timestamp = {2014.10.15},
Url = {http://dx.doi.org/10.1016/j.rse.2012.11.010}
}

We discuss statistical concerns regarding evaluation of three types of individual tree competition indices (non-spatially, spatially explicit and based on airborne laser scanning), with special attention to the method of selection of competitors, and the spatial dependency and smoothing caused by overlapping samples of competitors. We quantify the effect of spatial autocorrelation on the effective sample size for various search methods, to reveal potential type I statistical error, for a sample of 557 plots of the Norwegian National Forest Inventory located in the Hedmark Country. Our results show that spatial autocorrelation mostly appears when competitors are selected within short search radii (3Ã¢â‚¬â€œ4) m of the subject tree. However, when simultaneously accounting for the impact of spatial autocorrelation on the effective sample size between individual tree growth at breast height and competition, the effect appears to be neglect-able. This result is verified by testing if the change in the effective degrees of freedom in the Spearman rank correlation t-test for the Clifford et al. correction and a spatial bootstrap method, relative to the classical t-test effective degrees of freedom, are correlated with different measures of stand structure. This ratio showed no systematic variation across measures of plot micro and macro-scale variation like LoreyÅâ€º mean height, the Gini-coefficient of tree basal area or volume per hectare. The conclusion seems indifferent to plot edge bias correction. A linear mixed model with spatial covariance structure confirmed that sample overlap does not cause serious spatial dependence. Moreover, a median based statistical test revealed a significant smoothing effect, with increasing search radii of competitors, which causes loss of variation. However, the smoothing does not decrease the ability of the competition indices to correlate with individual tree growth at breast height within search radii of 12 m, and thus it does not represent any problem for prediction.

@Article{Pedersen2013,
Title = {On the evaluation of competition indices - The problem of overlapping samples},
Author = {Pedersen, Rune Østergaard and Næsset, Erik and Gobakken, Terje and Bollandsås, Ole Martin},
Journal = {Forest Ecology and Management},
Year = {2013},
Number = {0},
Pages = {120-133},
Volume = {310},
Abstract = {We discuss statistical concerns regarding evaluation of three types of individual tree competition indices (non-spatially, spatially explicit and based on airborne laser scanning), with special attention to the method of selection of competitors, and the spatial dependency and smoothing caused by overlapping samples of competitors. We quantify the effect of spatial autocorrelation on the effective sample size for various search methods, to reveal potential type I statistical error, for a sample of 557 plots of the Norwegian National Forest Inventory located in the Hedmark Country. Our results show that spatial autocorrelation mostly appears when competitors are selected within short search radii (3Ã¢â‚¬â€œ4) m of the subject tree. However, when simultaneously accounting for the impact of spatial autocorrelation on the effective sample size between individual tree growth at breast height and competition, the effect appears to be neglect-able. This result is verified by testing if the change in the effective degrees of freedom in the Spearman rank correlation t-test for the Clifford et al. correction and a spatial bootstrap method, relative to the classical t-test effective degrees of freedom, are correlated with different measures of stand structure. This ratio showed no systematic variation across measures of plot micro and macro-scale variation like LoreyÅâ€º mean height, the Gini-coefficient of tree basal area or volume per hectare. The conclusion seems indifferent to plot edge bias correction. A linear mixed model with spatial covariance structure confirmed that sample overlap does not cause serious spatial dependence. Moreover, a median based statistical test revealed a significant smoothing effect, with increasing search radii of competitors, which causes loss of variation. However, the smoothing does not decrease the ability of the competition indices to correlate with individual tree growth at breast height within search radii of 12 m, and thus it does not represent any problem for prediction.},
Doi = {10.1016/j.foreco.2013.07.040},
Keywords = {Plot edge bias National forest inventory Airborne laser scanning Spatial autocorrelation Competition indices},
Owner = {hanso},
Timestamp = {2014.10.15},
Url = {http://dx.doi.org/10.1016/j.foreco.2013.07.040}
}

This study presents a cost-sensitive active learning method for optimizing the field surveys by a human expert in the classification of single tree species using hyperspectral images. The goal of the proposed method is to guide the human expert in the collection of labeled samples in order to maximize the ratio between the classification accuracy with respect to the travelling costs. Experiments carried out in the context of a real study on forest inventory show the effectiveness of the proposed method.

In this study, detection success rates were evaluated for cultural remains that were detected manually based on interpretation of digital terrain models (DTM) derived from airborne laser scanning data and with a resolution of 1, 5 and 10Ã‚Â pointsÃ‚Â mÃ¢Ë†â€™2. The group of cultural remains included charcoal kilns, charcoal pits, hollow-roads, various pits, house foundations, tar kilns, grave mounds and pit-falls. The effects on the interpretation success of different types of cultural remains and their physical properties were studied: size, shape and elevation difference showing that the detection success rates varied considerably. The main tendency was that large cultural remains with clear geometrical shape (ovals and circles) and large elevation difference were much more successfully detected and classified compared to the smaller ones, especially those without a clear geometrical shape. The study also showed that it was the identification of the larger structures which profited most from an increased resolution of the DTM, and it was of no help to increase resolution in order to improve the identification of the irregularly shaped cultural remains.

@Article{RisbA¸l2013,
Title = {Interpreting cultural remains in airborne laser scanning generated digital terrain models: effects of size and shape on detection success rates},
Author = {Risbøl, Ole and Bollandsås, Ole Martin and Nesbakken, Anneli and Ørka, Hans Ole and Næsset, Erik and Gobakken, Terje},
Journal = {Journal of Archaeological Science},
Year = {2013},
Number = {12},
Pages = {4688-4700},
Volume = {40},
Abstract = {In this study, detection success rates were evaluated for cultural remains that were detected manually based on interpretation of digital terrain models (DTM) derived from airborne laser scanning data and with a resolution of 1, 5 and 10Ã‚Â pointsÃ‚Â mÃ¢Ë†â€™2. The group of cultural remains included charcoal kilns, charcoal pits, hollow-roads, various pits, house foundations, tar kilns, grave mounds and pit-falls. The effects on the interpretation success of different types of cultural remains and their physical properties were studied: size, shape and elevation difference showing that the detection success rates varied considerably. The main tendency was that large cultural remains with clear geometrical shape (ovals and circles) and large elevation difference were much more successfully detected and classified compared to the smaller ones, especially those without a clear geometrical shape. The study also showed that it was the identification of the larger structures which profited most from an increased resolution of the DTM, and it was of no help to increase resolution in order to improve the identification of the irregularly shaped cultural remains.},
Doi = {10.1016/j.jas.2013.07.002},
Keywords = {Airborne laser scanning Digital terrain models Cultural remains Forested areas Detection success rates Classification success rates Physical properties of cultural remains},
Owner = {hanso},
Timestamp = {2016.03.01},
Url = {http://dx.doi.org/10.1016/j.jas.2013.07.002}
}

The purpose of the study was to evaluate tree species composition estimated using combinations of different remotely sensed data with different inventory approaches for a forested area in Norway. Basal area species composition was estimated as both species proportions and main species by using data from airborne laser scanning (ALS) and airborne (multispectral and hyperspectral) imagery as auxiliary information in combination with three different inventory approaches: individual tree crown (ITC) approach; semi-individual tree crown (SITC) approach; and area-based approach (ABA). The main tree species classification obtained an overall accuracy higher than 86% for all ABA alternatives and for the two other inventory approaches (ITC and SITC) when combining ALS and hyperspectral imagery. The correlation between estimated species proportions and species proportions measured in the field was higher for coniferous species than for deciduous species and increased with the spectral resolution used. Especially, the ITC approach provided more accurate information regarding the proportion of deciduous species that occurred only in small proportions in the study area. Furthermore, the species proportion estimates of 83% of the plots deviated from field measured species proportions by two-tenths or less. Thus, species composition could be accurately estimated using the different approaches and the highest levels of accuracy were attained when ALS was used in combination with hyperspectral imagery. The accuracies obtained using the ABA in combination with only ALS data were encouraging for implementation in operational forest inventories.

@Article{Oerka2013,
Title = {Characterizing forest species composition using multiple remote sensing data sources and inventory approaches},
Author = {Ørka, Hans Ole and Dalponte, Michele and Gobakken, Terje and Næsset, Erik and Ene, Liviu Theodor},
Journal = {Scandinavian Journal of Forest Research},
Year = {2013},
Number = {7},
Pages = {677-688},
Volume = {28},
Abstract = {The purpose of the study was to evaluate tree species composition estimated using combinations of different remotely sensed data with different inventory approaches for a forested area in Norway. Basal area species composition was estimated as both species proportions and main species by using data from airborne laser scanning (ALS) and airborne (multispectral and hyperspectral) imagery as auxiliary information in combination with three different inventory approaches: individual tree crown (ITC) approach; semi-individual tree crown (SITC) approach; and area-based approach (ABA). The main tree species classification obtained an overall accuracy higher than 86% for all ABA alternatives and for the two other inventory approaches (ITC and SITC) when combining ALS and hyperspectral imagery. The correlation between estimated species proportions and species proportions measured in the field was higher for coniferous species than for deciduous species and increased with the spectral resolution used. Especially, the ITC approach provided more accurate information regarding the proportion of deciduous species that occurred only in small proportions in the study area. Furthermore, the species proportion estimates of 83% of the plots deviated from field measured species proportions by two-tenths or less. Thus, species composition could be accurately estimated using the different approaches and the highest levels of accuracy were attained when ALS was used in combination with hyperspectral imagery. The accuracies obtained using the ABA in combination with only ALS data were encouraging for implementation in operational forest inventories.},
Doi = {10.1080/02827581.2013.793386},
Keywords = {Species composition airborne laser scanning forest inventory hyperspectral imagery multispectral imagery tree species},
Owner = {hanso},
Timestamp = {2014.10.15},
Url = {http://dx.doi.org/10.1080/02827581.2013.793386}
}

Airborne laser scanning data and corresponding field data were acquired from boreal forests in Norway and Sweden, coniferous and broadleaved forests in Germany and tropical pulpwood plantations in Brazil. Treetop positions were extracted using six different algorithms developed in Finland, Germany, Norway and Sweden, and the accuracy of tree detection and height estimation was assessed. Furthermore, the weaknesses and strengths of the methods under different types of forest were analyzed. The results showed that forest structure strongly affected the performance of all algorithms. Particularly, the success of tree detection was found to be dependent on tree density and clustering. The differences in performance between methods were more pronounced for tree detection than for height estimation. The algorithms showed a slightly better performance in the conditions for which they were developed, while some could be adapted by different parameterization according to training with local data. The results of this study may help guiding the choice of method under different forest types and may be of great value for future refinement of the single-tree detection algorithms.

The subalpine zone is the transition between forest and alpine vegetation communities. In Norway, as in many other nations, low productivity or non-merchantable forests, like the subalpine zone, are not routinely subject to inventory programs. Awareness of expected changes in the sub-alpine zone as a result of a warmer climate, and the interest in full carbon accounting at the national level, has dictated a need for data capture in these mountainous areas. We propose an approach for integrating strip samples of Light Detection and Ranging (LiDAR) data with Landsat imagery to delineate the subalpine zone. In the current study the subalpine zone was defined according to international definitions based on tree heights and canopy cover. The three-dimensional measurements of forest structure obtained from LiDAR enable a delineation of the subalpine zone. The approach was implemented using 53 LiDAR sample strips in Hedmark County, Norway, and validated with field measurements at 26 locations. The subalpine zone boundaries were found to be within one Landsat pixel, on average, when validated using an image gradient technique. Furthermore, binomial logistic regression was used to upscale the LiDAR classes to the entire county (27&#xa0;400&#xa0;km2) using satellite images and information derived from a digital terrain model. The result from the binomial logistic regression was a probability map suitable for monitoring changes in the extent and location of the subalpine zone. The probability surface was separated into hard classes by calibrated alpha-cuts derived using density estimation to support the information needs of inventory stratification and area estimation.

@Article{Oerka2012a,
Title = {Subalpine zone delineation using LiDAR and Landsat imagery},
Author = {Hans Ole Ørka and Michael A. Wulder and Terje Gobakken and Erik Næsset},
Journal = {Remote Sensing of Environment},
Year = {2012},
Number = {0},
Pages = {11 - 20},
Volume = {119},
Abstract = {The subalpine zone is the transition between forest and alpine vegetation communities. In Norway, as in many other nations, low productivity or non-merchantable forests, like the subalpine zone, are not routinely subject to inventory programs. Awareness of expected changes in the sub-alpine zone as a result of a warmer climate, and the interest in full carbon accounting at the national level, has dictated a need for data capture in these mountainous areas. We propose an approach for integrating strip samples of Light Detection and Ranging (LiDAR) data with Landsat imagery to delineate the subalpine zone. In the current study the subalpine zone was defined according to international definitions based on tree heights and canopy cover. The three-dimensional measurements of forest structure obtained from LiDAR enable a delineation of the subalpine zone. The approach was implemented using 53 LiDAR sample strips in Hedmark County, Norway, and validated with field measurements at 26 locations. The subalpine zone boundaries were found to be within one Landsat pixel, on average, when validated using an image gradient technique. Furthermore, binomial logistic regression was used to upscale the LiDAR classes to the entire county (27&#xa0;400&#xa0;km2) using satellite images and information derived from a digital terrain model. The result from the binomial logistic regression was a probability map suitable for monitoring changes in the extent and location of the subalpine zone. The probability surface was separated into hard classes by calibrated alpha-cuts derived using density estimation to support the information needs of inventory stratification and area estimation.},
Doi = {10.1016/j.rse.2011.11.023},
ISSN = {0034-4257},
Keywords = {Subalpine zone},
Owner = {hanso},
Timestamp = {2012.01.17},
Url = {http://www.sciencedirect.com/science/article/pii/S0034425711004202}
}

Conservation of biodiversity requires information at many spatial scales in order to detect and preserve habitat for many species, often simultaneously. Vegetation structure information is particularly important for avian habitat models and has largely been unavailable for large areas at the desired resolution. Airborne LiDAR, with its combination of relatively broad coverage and fine resolution provides existing new opportunities to map vegetation structure and hence avian habitat. Our goal was to model the richness of forest songbirds using forest structure information obtained from LiDAR data. In deciduous forests of southern Wisconsin, USA, we used discrete-return airborne LiDAR to derive forest structure metrics related to the height and density of vegetation returns, as well as composite variables that captured major forest structural elements. We conducted point counts to determine total forest songbird richness and the richness of foraging, nesting, and forest edge-related habitat guilds. A suite of 35 LiDAR variables were used to model bird species richness using best-subsets regression and we used hierarchical partitioning analysis to quantify the explanatory power of each variable in the multivariate models. Songbird species richness was correlated most strongly with LiDAR variables related to canopy and midstory height and midstory density (R2&#xa0;=&#xa0;0.204, p&#xa0;&lt;&#xa0;0.001). Richness of species that nest in the midstory was best explained by canopy height variables (R2&#xa0;=&#xa0;0.197, p&#xa0;&lt;&#xa0;0.001). Species that forage on the ground responded to mean canopy height and the height of the lower canopy (R2&#xa0;=&#xa0;0.149, p&#xa0;&lt;&#xa0;0.005) while aerial foragers had higher richness where the canopy was tall and dense and the midstory more sparse (R2&#xa0;=&#xa0;0.216, p&#xa0;&lt;&#xa0;0.001). Richness of edge-preferring species was greater where there were fewer vegetation returns but higher density in the understory (R2&#xa0;=&#xa0;0.153, p&#xa0;&lt;&#xa0;0.005). Forest interior specialists responded positively to a tall canopy, developed midstory, and a higher proportion of vegetation returns (R2&#xa0;=&#xa0;0.195, p&#xa0;&lt;&#xa0;0.001). LiDAR forest structure metrics explained between 15 and 20% of the variability in richness within deciduous forest songbird communities. This variability was associated with vertical structure alone and shows how LiDAR can provide a source of complementary predictive data that can be incorporated in models of wildlife habitat associations across broad geographical extents.

@Article{Lesak2011,
Title = {Modeling forest songbird species richness using LiDAR-derived measures of forest structure},
Author = {Lesak, Adrian A. and Radeloff, Volker C. and Hawbaker, Todd J. and Pidgeon, Anna M. and Gobakken, Terje and Contrucci, Kirk},
Journal = {Remote Sensing of Environment},
Year = {2011},
Number = {11},
Pages = {2823-2835},
Volume = {115},
Abstract = {Conservation of biodiversity requires information at many spatial scales in order to detect and preserve habitat for many species, often simultaneously. Vegetation structure information is particularly important for avian habitat models and has largely been unavailable for large areas at the desired resolution. Airborne LiDAR, with its combination of relatively broad coverage and fine resolution provides existing new opportunities to map vegetation structure and hence avian habitat. Our goal was to model the richness of forest songbirds using forest structure information obtained from LiDAR data. In deciduous forests of southern Wisconsin, USA, we used discrete-return airborne LiDAR to derive forest structure metrics related to the height and density of vegetation returns, as well as composite variables that captured major forest structural elements. We conducted point counts to determine total forest songbird richness and the richness of foraging, nesting, and forest edge-related habitat guilds. A suite of 35 LiDAR variables were used to model bird species richness using best-subsets regression and we used hierarchical partitioning analysis to quantify the explanatory power of each variable in the multivariate models. Songbird species richness was correlated most strongly with LiDAR variables related to canopy and midstory height and midstory density (R2&#xa0;=&#xa0;0.204, p&#xa0;&lt;&#xa0;0.001). Richness of species that nest in the midstory was best explained by canopy height variables (R2&#xa0;=&#xa0;0.197, p&#xa0;&lt;&#xa0;0.001). Species that forage on the ground responded to mean canopy height and the height of the lower canopy (R2&#xa0;=&#xa0;0.149, p&#xa0;&lt;&#xa0;0.005) while aerial foragers had higher richness where the canopy was tall and dense and the midstory more sparse (R2&#xa0;=&#xa0;0.216, p&#xa0;&lt;&#xa0;0.001). Richness of edge-preferring species was greater where there were fewer vegetation returns but higher density in the understory (R2&#xa0;=&#xa0;0.153, p&#xa0;&lt;&#xa0;0.005). Forest interior specialists responded positively to a tall canopy, developed midstory, and a higher proportion of vegetation returns (R2&#xa0;=&#xa0;0.195, p&#xa0;&lt;&#xa0;0.001). LiDAR forest structure metrics explained between 15 and 20% of the variability in richness within deciduous forest songbird communities. This variability was associated with vertical structure alone and shows how LiDAR can provide a source of complementary predictive data that can be incorporated in models of wildlife habitat associations across broad geographical extents.},
Doi = {10.1016/j.rse.2011.01.025},
Keywords = {Bird species richness Forest structure Airborne LiDAR Deciduous forest Guild richness Wisconsin},
Owner = {hanso},
Timestamp = {2016.03.01},
Url = {http://www.sciencedirect.com/science/article/pii/S0034425711001271}
}

The aim of this paper was to examine different plot selection strategies of field training plots in forest inventory using airborne laser scanner (ALS) data. The applied plot selection strategies were random selection, random selection within pre-stratification according to forest type, selection of plots according to geographical location and selection of plots based on properties of the ALS data given as a priori information. The study was conducted by means of simulation utilizing existing and independent training and validation plot data and the performance was evaluated by assessing bias and the root mean square error (RMSE). The accuracy of simultaneously derived biophysical stand properties, i.e. volume, number of stems and LoreyÃ¢â‚¬â„¢s mean height, was examined using non-parametric modelling. The use of ALS data as a priori information provided the most accurate results in the case of stand volume and number of stems the RMSE being less than 15 and 30 per cent, respectively. For the mean height, also the other selection strategies were as good but the most accurate alternative varied according to number of training plots used. In most cases, the RMSE values for the mean height were between 8 and 9 per cent. The bias of the different strategies followed the same patterns as the corresponding RMSE values.

Total above-ground biomass of spruce, pine and birch was estimated in three different field datasets collected in young forests in south-east Norway. The mean heights ranged from 1.77 to 9.66 m. These field data were regressed against metrics derived from canopy height distributions generated from airborne laser scanner (ALS) data with a point density of 0.9Ã¢â‚¬â€œ1.2 m<sup>-2</sup>. The field data consisted of 79 plots with size 200Ã¢â‚¬â€œ232.9 m<sup>2</sup> and 20 stands with an average size of 3742 m<sup>2</sup>. Total above-ground biomass ranged from 2.27 to 90.42 Mg ha<sup>-1</sup>. The influences of (1) regression model form, (2) canopy threshold value and (3) tree species on the relationships between biomass and ALS-derived metrics were assessed. The analysed model forms were multiple linear models, models with logarithmic transformation of the response and explanatory variables, and models with square root transformation of the response. The different canopy thresholds considered were fixed values of 0.5, 1.3 and 2.0 m defining the limit between laser canopy echoes and below-canopy echoes. The proportion of explained variability of the estimated models ranged from 60% to 83%. Tree species had a significant influence on the models. For given values of the ALS-derived metrics related to canopy height and canopy density, spruce tended to have higher above-ground biomass values than pine and deciduous species. There were no clear effects of model form and canopy threshold on the accuracy of predictions produced by cross validation of the various models, but there is a risk of heteroskedasticity with linear models. Cross validation revealed an accuracy of the root mean square error (RMSE) ranging from 3.85 to 13.9 Mg ha<sup>-1</sup>, corresponding to 22.6% to 48.1% of mean field-measured biomass. It was concluded that airborne laser scanning has a potential for predicting biomass in young forest stands (&gt; 0.5 ha) with an accuracy of 20Ã¢â‚¬â€œ30% of mean ground value.

@Article{NA¦sset2011,
Title = {Estimating above-ground biomass in young forests with airborne laser scanning},
Author = {Næsset, Erik},
Journal = {International Journal of Remote Sensing},
Year = {2011},
Number = {2},
Pages = {473 - 501},
Volume = {32},
Abstract = {Total above-ground biomass of spruce, pine and birch was estimated in three different field datasets collected in young forests in south-east Norway. The mean heights ranged from 1.77 to 9.66 m. These field data were regressed against metrics derived from canopy height distributions generated from airborne laser scanner (ALS) data with a point density of 0.9Ã¢â‚¬â€œ1.2 m<sup>-2</sup>. The field data consisted of 79 plots with size 200Ã¢â‚¬â€œ232.9 m<sup>2</sup> and 20 stands with an average size of 3742 m<sup>2</sup>. Total above-ground biomass ranged from 2.27 to 90.42 Mg ha<sup>-1</sup>. The influences of (1) regression model form, (2) canopy threshold value and (3) tree species on the relationships between biomass and ALS-derived metrics were assessed. The analysed model forms were multiple linear models, models with logarithmic transformation of the response and explanatory variables, and models with square root transformation of the response. The different canopy thresholds considered were fixed values of 0.5, 1.3 and 2.0 m defining the limit between laser canopy echoes and below-canopy echoes. The proportion of explained variability of the estimated models ranged from 60% to 83%. Tree species had a significant influence on the models. For given values of the ALS-derived metrics related to canopy height and canopy density, spruce tended to have higher above-ground biomass values than pine and deciduous species. There were no clear effects of model form and canopy threshold on the accuracy of predictions produced by cross validation of the various models, but there is a risk of heteroskedasticity with linear models. Cross validation revealed an accuracy of the root mean square error (RMSE) ranging from 3.85 to 13.9 Mg ha<sup>-1</sup>, corresponding to 22.6% to 48.1% of mean field-measured biomass. It was concluded that airborne laser scanning has a potential for predicting biomass in young forest stands (&gt; 0.5 ha) with an accuracy of 20Ã¢â‚¬â€œ30% of mean ground value.},
Doi = {10.1080/01431160903474970},
Owner = {hanso},
Timestamp = {2011.11.17},
Url = {http://www.tandfonline.com/doi/abs/10.1080/01431160903474970?journalCode=tres20}
}

In this paper, we analyse how optimal forest management of even aged Norway spruce changes when economic values are placed on carbon fixation, release, and saved greenhouse gas emissions from using wood instead of more energy intensive materials or fossil fuels. The analyses are done for three different site qualities in Norway, assuming present climate and with a range of CO2 prices and real rates of return. Compared to current recommended management, the optimal number of plants per ha and harvest age are considerably higher when carbon benefits are included, and increase with increasing price on CO2. Furthermore, planting becomes more favourable compared to natural regeneration. At the medium site quality, assuming 2% p.a. real rate of return and 20 euros per ton CO2, optimal planting density increases from 1500 per ha to 3000 per ha. Optimal harvest age increases from 90 to 140 years. Including saved greenhouse gas emissions when wood is used instead of more energy intensive materials or fossil fuels, i.e. substitution effects, does not affect optimal planting density much, but implies harvesting up to 20 years earlier. The value of the forest area increases with increasing price on CO2, and most of the income is from carbon. By using the current recommended management in calculations of carbon benefit, our results indicate that the forestÃ¢â‚¬â„¢s potential to provide this environmental good is underestimated. The study includes many uncertain factors. Highest uncertainty is related to the accuracy of the forest growth and mortality functions at high stand ages and densities, and that albedo effects and future climate changes are not considered. As such, the results should be viewed as exploratory and not normative.

@Article{Raymer2011,
Title = {Optimal forest management with carbon benefits included},
Author = {Raymer, Ann Kristin and Gobakken, Terje and Solberg, Birger},
Journal = {SILVA FENNICA},
Year = {2011},
Number = {3},
Pages = {395Ã¢â‚¬â€œ414},
Volume = {45},
Abstract = {In this paper, we analyse how optimal forest management of even aged Norway spruce changes when economic values are placed on carbon fixation, release, and saved greenhouse gas emissions from using wood instead of more energy intensive materials or fossil fuels. The analyses are done for three different site qualities in Norway, assuming present climate and with a range of CO2 prices and real rates of return. Compared to current recommended management, the optimal number of plants per ha and harvest age are considerably higher when carbon benefits are included, and increase with increasing price on CO2. Furthermore, planting becomes more favourable compared to natural regeneration. At the medium site quality, assuming 2% p.a. real rate of return and 20 euros per ton CO2, optimal planting density increases from 1500 per ha to 3000 per ha. Optimal harvest age increases from 90 to 140 years. Including saved greenhouse gas emissions when wood is used instead of more energy intensive materials or fossil fuels, i.e. substitution effects, does not affect optimal planting density much, but implies harvesting up to 20 years earlier. The value of the forest area increases with increasing price on CO2, and most of the income is from carbon. By using the current recommended management in calculations of carbon benefit, our results indicate that the forestÃ¢â‚¬â„¢s potential to provide this environmental good is underestimated. The study includes many uncertain factors. Highest uncertainty is related to the accuracy of the forest growth and mortality functions at high stand ages and densities, and that albedo effects and future climate changes are not considered. As such, the results should be viewed as exploratory and not normative.},
Keywords = {CO2 greenhouse gas mitigation forest management optimisation wood products substitution Norway spruce},
Owner = {hanso},
Timestamp = {2014.10.15}
}

While forest inventories based on airborne laser scanning data (ALS) using the area based approach (ABA) have reached operational status, methods using the individual tree crown approach (ITC) have basically remained a research issue. One of the main obstacles for operational applications of ITC is biased results often experienced due to segmentation errors. In this article, we propose a new method, called "semi-ITC" that overcomes the main problems related to ITC by imputing ground truth data within crown segments from the nearest neighboring segment. This may be none, one, or several trees. The distances between segments were derived based on a set of explanatory variables using two nonparametric methods, i.e., most similar neighbor inference (MSN) and random forest (RF). RF favored the imputation of common observations in the data set which resulted in significant biases. Main conclusions are therefore based on MSN. The explanatory variables were calculated by means of small footprint ALS and multispectral data. When testing with empirical data the new method compared favorably to the well-known ABA. Another advantage of the new method over the ABA is that it allowed for the modeling of rare tree species. The results of predicting timber volume with the semi-ITC method were unbiased and the root mean squared error (RMSE) on plot level was smaller than the standard deviation of the observed response variables. The relative RMSEs after cross validation using semi-ITC for total volume and volume of the individual species pine, spruce, birch, and aspen on plot level were 17, 38, 40, 101, and 222%, respectively. Due to the unbiasedness of the estimation, this study is a showcase for how to use crown segments resulting from ITC algorithms in a forest inventory context. (C) 2009 Elsevier Inc. All rights reserved.

@Article{Breidenbach2010,
Title = {Prediction of species specific forest inventory attributes using a nonparametric semi-individual tree crown approach based on fused airborne laser scanning and multispectral data},
Author = {Breidenbach, J. and Næsset, E. and Lien, V. and Gobakken, T. and Solberg, S.},
Journal = {Remote Sensing of Environment},
Year = {2010},
Note = {Breidenbach, Johannes Naesset, Erik Lien, Vegard Gobakken, Terje Solberg, Svein},
Number = {4},
Pages = {911-924},
Volume = {114},
Abstract = {While forest inventories based on airborne laser scanning data (ALS) using the area based approach (ABA) have reached operational status, methods using the individual tree crown approach (ITC) have basically remained a research issue. One of the main obstacles for operational applications of ITC is biased results often experienced due to segmentation errors. In this article, we propose a new method, called "semi-ITC" that overcomes the main problems related to ITC by imputing ground truth data within crown segments from the nearest neighboring segment. This may be none, one, or several trees. The distances between segments were derived based on a set of explanatory variables using two nonparametric methods, i.e., most similar neighbor inference (MSN) and random forest (RF). RF favored the imputation of common observations in the data set which resulted in significant biases. Main conclusions are therefore based on MSN. The explanatory variables were calculated by means of small footprint ALS and multispectral data. When testing with empirical data the new method compared favorably to the well-known ABA. Another advantage of the new method over the ABA is that it allowed for the modeling of rare tree species. The results of predicting timber volume with the semi-ITC method were unbiased and the root mean squared error (RMSE) on plot level was smaller than the standard deviation of the observed response variables. The relative RMSEs after cross validation using semi-ITC for total volume and volume of the individual species pine, spruce, birch, and aspen on plot level were 17, 38, 40, 101, and 222%, respectively. Due to the unbiasedness of the estimation, this study is a showcase for how to use crown segments resulting from ITC algorithms in a forest inventory context. (C) 2009 Elsevier Inc. All rights reserved.},
Owner = {hanso},
Timestamp = {2011.11.17}
}

Recently, the intensity characteristics of discrete-return LiDAR sensors were studied for vegetation classification. We examined two normalization procedures affecting LiDAR intensity through the scanning geometry and the system settings, namely, range normalization and the effects of the automatic gain control (AGC) in the Optech ALTM3100 and Leica ALS50-II sensors. Range normalization corresponds to weighting of the observed intensities with the term (R/R-Ref)(a), where R is the range, R-Ref is a mean reference range, and a is an element of [2, 4] is the exponent that is, according to theory, dependent on the target geometry. LiDAR points belonging to individual tree crowns were extracted for 13 887 trees in southern Finland. The coefficient of variation (CV) of the intensity was analyzed for a range of values of exponent a. The tree species classification performance using 13 intensity variables was also used for sensitivity analysis of the effect of a. The results were in line with the established theory, since the optimal level of a was lower (a approximate to 2) for trees with large or clumped leaves and higher (a approximate to 3) for diffuse coniferous crowns. Different echo groups also showed varying responses. Single-return pulses that represented strong reflections had a lower optimal value of a than the first and all echoes in a pulse. The gain in classification accuracy from the optimal selection of the exponent was 2%-3%, and the optimum for classification was different from that obtained using the CV analysis. In the ALS50-II sensor, the combined and optimized AGC and R normalizations had a notably larger effect (6%-9%) on classification accuracy. Our study demonstrates the ambiguity of R normalization in vegetation canopies. (C) 2010 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

@Article{Korpela2010,
Title = {Range and AGC normalization in airborne discrete-return LiDAR intensity data for forest canopies},
Author = {Korpela, I. and Ørka, H. O. and HyyppÃ¤, J. and Heikkinen, V. and Tokola, T.},
Journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
Year = {2010},
Note = {ISI Document Delivery No.: 627EN Times Cited: 0 Cited Reference Count: 39 Korpela, Ilkka Orka, Hans Ole Hyyppa, Juha Heikkinen, Ville Tokola, Timo universities of Joensuu and Helsinki ; Finnish Forest Research Institute ; Finnish Geodetic Institute ; Suomen Luonnonvarain Tutkimussaatio ; University of Helsinki We thank the teachers, students, and research assistants, who have participated in the field data measurements in 1997-2008 at Hyytiala. The two anonymous reviewers of the manuscript are acknowledged for their good comments. The aerial imaging and LiDAR campaigns which we applied were paid for by several forestry companies, the universities of Joensuu and Helsinki, the Finnish Forest Research Institute, and the Finnish Geodetic Institute. Suomen Luonnonvarain Tutkimussaatio and the University of Helsinki supported the research work of Ilkka Korpela during this study. ELSEVIER SCIENCE BV AMSTERDAM},
Number = {4},
Pages = {369-379},
Volume = {65},
Abstract = {Recently, the intensity characteristics of discrete-return LiDAR sensors were studied for vegetation classification. We examined two normalization procedures affecting LiDAR intensity through the scanning geometry and the system settings, namely, range normalization and the effects of the automatic gain control (AGC) in the Optech ALTM3100 and Leica ALS50-II sensors. Range normalization corresponds to weighting of the observed intensities with the term (R/R-Ref)(a), where R is the range, R-Ref is a mean reference range, and a is an element of [2, 4] is the exponent that is, according to theory, dependent on the target geometry. LiDAR points belonging to individual tree crowns were extracted for 13 887 trees in southern Finland. The coefficient of variation (CV) of the intensity was analyzed for a range of values of exponent a. The tree species classification performance using 13 intensity variables was also used for sensitivity analysis of the effect of a. The results were in line with the established theory, since the optimal level of a was lower (a approximate to 2) for trees with large or clumped leaves and higher (a approximate to 3) for diffuse coniferous crowns. Different echo groups also showed varying responses. Single-return pulses that represented strong reflections had a lower optimal value of a than the first and all echoes in a pulse. The gain in classification accuracy from the optimal selection of the exponent was 2%-3%, and the optimum for classification was different from that obtained using the CV analysis. In the ALS50-II sensor, the combined and optimized AGC and R normalizations had a notably larger effect (6%-9%) on classification accuracy. Our study demonstrates the ambiguity of R normalization in vegetation canopies. (C) 2010 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.},
Keywords = {Vegetation Forestry Radiometry Laser scanning Classification LASER SCANNER DATA INDIVIDUAL TREES SMALL-FOOTPRINT LEAF-OFF STANDS CLASSIFICATION CALIBRATION},
Owner = {hanso},
Timestamp = {2011.11.17}
}

Tree species identification constitutes a bottleneck in remote sensing-based forest inventory. In passive images the differentiating features overlap and bidirectional reflectance hampers analysis. Airborne LiDAR provides radiometric and geometric information. We examined the single-trees-level response of two LiDAR sensors in over 13000 forest trees in southern Finland. We focused on the commercially important species. Our aims were to 1) explore the relevant LiDAR features and study their dependencies on stand and tree variables, 2) examine two sensors and their fusion, 3) quantify the gain from intensity normalizations, 4) examine the importance of the size of the training set, and 5) determine the effects of stand age and site fertility. A set of 570 semiurban broad-leaved trees and exotic conifers was analyzed to 6) examine the LiDAR signal in the economically less important species. An accuracy of 88-90% was achieved in the classification of Scots pine, Norway spruce, and birch, using intensity variables. Spruce and birch showed the highest levels of confusion. Downsizing the training set from 30% to 2.5% of all trees had only a marginal effect on the performance of classifiers. The intensity features were dependent on the absolute and relative sizes of trees, especially for birch. The results suggest that leaf size, orientation, and foliage density affect the intensity, which is thus not affected by reflectance only. Some of the ecologically important species in Finland may be separable, since they gave rise to high intensity values. Comparison of the sensors implies that performance of the intensity data for species classification varies between sensors for reasons that remained uncertain. Both range and gain receiver normalization improved species classification. Weighting of the intensity values improved the fusion of two LiDAR datasets.

@Article{Korpela2010a,
Title = {Tree species classification using airborne LiDAR - Effects of stand and tree parameters, downsizing of training set, intensity normalization, and sensor type},
Author = {Korpela, I. and Ørka, H. O. and Maltamo, M. and Tokola, T. and HyyppÃ¤, J.},
Journal = {Silva Fennica},
Year = {2010},
Note = {ISI Document Delivery No.: 620LU Times Cited: 0 Cited Reference Count: 45 Korpela, Ilkka Orka, Hans Ole Maltamo, Matti Tokola, Timo Hyyppa, Juha FINNISH SOC Forest ScienceFINNISH FOREST RESEARCH HELSINKI},
Number = {2},
Pages = {319-339},
Volume = {44},
Abstract = {Tree species identification constitutes a bottleneck in remote sensing-based forest inventory. In passive images the differentiating features overlap and bidirectional reflectance hampers analysis. Airborne LiDAR provides radiometric and geometric information. We examined the single-trees-level response of two LiDAR sensors in over 13000 forest trees in southern Finland. We focused on the commercially important species. Our aims were to 1) explore the relevant LiDAR features and study their dependencies on stand and tree variables, 2) examine two sensors and their fusion, 3) quantify the gain from intensity normalizations, 4) examine the importance of the size of the training set, and 5) determine the effects of stand age and site fertility. A set of 570 semiurban broad-leaved trees and exotic conifers was analyzed to 6) examine the LiDAR signal in the economically less important species. An accuracy of 88-90% was achieved in the classification of Scots pine, Norway spruce, and birch, using intensity variables. Spruce and birch showed the highest levels of confusion. Downsizing the training set from 30% to 2.5% of all trees had only a marginal effect on the performance of classifiers. The intensity features were dependent on the absolute and relative sizes of trees, especially for birch. The results suggest that leaf size, orientation, and foliage density affect the intensity, which is thus not affected by reflectance only. Some of the ecologically important species in Finland may be separable, since they gave rise to high intensity values. Comparison of the sensors implies that performance of the intensity data for species classification varies between sensors for reasons that remained uncertain. Both range and gain receiver normalization improved species classification. Weighting of the intensity values improved the fusion of two LiDAR datasets.},
Keywords = {airborne laser scanning ALS laser Optech ALTM3100 Leica ALS50-II canopy crown modeling monoplotting backscatter amplitude intensity discriminant analysis DISCRETE-RETURN LIDAR LASER-SCANNING DATA INDIVIDUAL TREES SMALL-FOOTPRINT AERIAL IMAGES FOREST INVENTORY LEAF-OFF IDENTIFICATION PHOTOGRAMMETRY METRICS},
Owner = {hanso},
Timestamp = {2011.11.17}
}

In this study, different methods were used to predict mean crown height of Norway spruce-dominated stands by means of low pulse density airborne laser scanning (ALS) data. The methods were based on statistical modelling, properties of the laser point clouds or combinations of them. Separate modelling data were used for model calibration and two different validation datasets were used to assess the accuracy of the results. The results obtained were partly contradictory, showing varying performance of different methods using different datasets. However, there were also notable differences between the methods used to obtain crown height by field measurements. The root mean square error figures of crown height predictions were at minimum between 1.0 and 1.5 m. This study showed that statistical modelling based on ALS height metrics was a good approach if the relationship between mean crown height and the ALS information was corresponding in the modelling data and in the application phase. A method based on the alpha shape technique was also an accurate alternative. Methods that rely directly on the laser point cloud to predict mean crown height without any calibration were good alternatives to get relatively accurate results but there are still drawbacks (area of calculation unit) in their applicability.

@Article{Maltamo2010,
Title = {Comparing different methods for prediction of mean crown height in Norway spruce stands using airborne laser scanner data},
Author = {Maltamo, M. and Bollandsås, O. M. and Vauhkonen, J. and Breidenbach, J. and Gobakken, T. and Næsset, E.},
Journal = {Forestry},
Year = {2010},
Number = {3},
Pages = {257-268},
Volume = {83},
Abstract = {In this study, different methods were used to predict mean crown height of Norway spruce-dominated stands by means of low pulse density airborne laser scanning (ALS) data. The methods were based on statistical modelling, properties of the laser point clouds or combinations of them. Separate modelling data were used for model calibration and two different validation datasets were used to assess the accuracy of the results. The results obtained were partly contradictory, showing varying performance of different methods using different datasets. However, there were also notable differences between the methods used to obtain crown height by field measurements. The root mean square error figures of crown height predictions were at minimum between 1.0 and 1.5 m. This study showed that statistical modelling based on ALS height metrics was a good approach if the relationship between mean crown height and the ALS information was corresponding in the modelling data and in the application phase. A method based on the alpha shape technique was also an accurate alternative. Methods that rely directly on the laser point cloud to predict mean crown height without any calibration were good alternatives to get relatively accurate results but there are still drawbacks (area of calculation unit) in their applicability.},
Owner = {hanso},
Timestamp = {2011.11.17}
}

Tremendous advances in the construction and assessment of forest attribute maps and related spatial products have been realized in recent years, partly as a result of the use of remotely sensed data as an information source. This review focuses on the current state of techniques for the construction and assessment of remote sensing-based maps and addresses five topic areas: statistical classification and prediction techniques used to construct maps and related spatial products, accuracy assessment methods, map-based statistical inference, and two emerging topics, change detection and use of lidar data. Multiple general conclusions were drawn from the review: (1) remotely sensed data greatly contribute to the construction of forest attribute maps and related spatial products and to the reduction of inventory costs; (2) parametric prediction techniques, accuracy assessment methods and probability-based (design-based) inferential methods are generally familiar and mature, although inference is surprisingly seldom addressed; (3) non-parametric prediction techniques and model-based inferential methods lack maturity and merit additional research; (4) change detection methods, with their great potential for adding a spatial component to change estimates, will mature rapidly; and (5) lidar applications, although currently immature, add an entirely new dimension to remote sensing research and will also mature rapidly. Crucial forest sustainability and climate change applications will continue to push all aspects of remote sensing to the forefront of forest research and operations.

@Article{McRoberts2010,
Title = {Using remotely sensed data to construct and assess forest attribute maps and related spatial products},
Author = {McRoberts, Ronald E. and Cohen, Warren B. and Næsset, Erik and Stehman, Stephen V. and Tomppo, Erkki O.},
Journal = {Scandinavian Journal of Forest Research},
Year = {2010},
Number = {4},
Pages = {340 - 367},
Volume = {25},
Abstract = {Tremendous advances in the construction and assessment of forest attribute maps and related spatial products have been realized in recent years, partly as a result of the use of remotely sensed data as an information source. This review focuses on the current state of techniques for the construction and assessment of remote sensing-based maps and addresses five topic areas: statistical classification and prediction techniques used to construct maps and related spatial products, accuracy assessment methods, map-based statistical inference, and two emerging topics, change detection and use of lidar data. Multiple general conclusions were drawn from the review: (1) remotely sensed data greatly contribute to the construction of forest attribute maps and related spatial products and to the reduction of inventory costs; (2) parametric prediction techniques, accuracy assessment methods and probability-based (design-based) inferential methods are generally familiar and mature, although inference is surprisingly seldom addressed; (3) non-parametric prediction techniques and model-based inferential methods lack maturity and merit additional research; (4) change detection methods, with their great potential for adding a spatial component to change estimates, will mature rapidly; and (5) lidar applications, although currently immature, add an entirely new dimension to remote sensing research and will also mature rapidly. Crucial forest sustainability and climate change applications will continue to push all aspects of remote sensing to the forefront of forest research and operations.},
Owner = {hanso},
Timestamp = {2011.11.17}
}

National forest inventories (NFIs) have a long history, although their current major features date only to the early years of the twentieth century. Recent issues such as concern over the effects of acid deposition, biodiversity, forest sustainability, increased demand for forest data, international reporting requirements and climate change have led to the expansion of NFIs to include more variables, greater diversity in sampling protocols and a generally more holistic approach. This review focuses on six selected topics: (1) a brief historical review; (2) a summary of common structural features of NFIs; (3) a brief review of international reporting requirements using NFI data with an emphasis on approaches to harmonized estimation; (4) an overview of inventory estimation methods that can be enhanced with remotely sensed data; (5) an overview of nearest neighbors prediction and estimation techniques; and (6) a brief overview of several emerging issues including carbon inventories in developing countries and use of lidar data. Although general inventory principles will remain unchanged, sampling designs, plot configurations and measurement protocols will require modification before they can be applied in countries with tropical forests. Technological advances, particularly in the use of remotely sensed data, including lidar data, have led to greater inventory efficiencies, better maps and accurate estimation for small areas.

@Article{McRoberts2010a,
Title = {Advances and emerging issues in national forest inventories},
Author = {McRoberts, Ronald E. and Tomppo, Erkki O. and Næsset, Erik},
Journal = {Scandinavian Journal of Forest Research},
Year = {2010},
Note = {Lest på flyet fra Spania 16032011,},
Number = {4},
Pages = {368 - 381},
Volume = {25},
Abstract = {National forest inventories (NFIs) have a long history, although their current major features date only to the early years of the twentieth century. Recent issues such as concern over the effects of acid deposition, biodiversity, forest sustainability, increased demand for forest data, international reporting requirements and climate change have led to the expansion of NFIs to include more variables, greater diversity in sampling protocols and a generally more holistic approach. This review focuses on six selected topics: (1) a brief historical review; (2) a summary of common structural features of NFIs; (3) a brief review of international reporting requirements using NFI data with an emphasis on approaches to harmonized estimation; (4) an overview of inventory estimation methods that can be enhanced with remotely sensed data; (5) an overview of nearest neighbors prediction and estimation techniques; and (6) a brief overview of several emerging issues including carbon inventories in developing countries and use of lidar data. Although general inventory principles will remain unchanged, sampling designs, plot configurations and measurement protocols will require modification before they can be applied in countries with tropical forests. Technological advances, particularly in the use of remotely sensed data, including lidar data, have led to greater inventory efficiencies, better maps and accurate estimation for small areas.},
Doi = {10.1080/02827581.2010.496739},
Owner = {hanso},
Timestamp = {2011.11.17},
Url = {http://www.tandfonline.com/doi/abs/10.1080/02827581.2010.496739}
}

Formerly, tree height has been more difficult to measure accurately in the field than tree diameter at breast height. As a consequence, models to predict height from diameter measurements have been widely developed in the forestry literature. Through the use of airborne laser scanning technology (e.g., LiDAR), tree variables such as height and crown diameter can be measured accurately, a development which has spawned the need for models to predict diameter from airborne laser-derived measurements. Although some work has been done for fitting such models, none have incorporated spatial information to improve the accuracy of the predicted diameters. Using a simple linear model for predicting tree diameter from laser-derived tree height and crown diameter measurements, we compared the performance of ordinary least squares (OLS), generalized least squares with a non-null correlation structure (GLS), linear mixed-effects model (LME), and geographically weighted regression (GWR). Our data were obtained from 36 sample plots established in Norway. This is the first study to examine the use of spatial statistical models for tree-level LiDAR data. Root mean square prediction errors in tree diameter with LME are 3.5%, with GWR are 10%, and with OLS and GLS are 17%. LME also exhibited low variability in predicting performance across all the validation classes (based on laser-derived height). Giving the difficulties of using parametric statistical inference (such as maximum likelihood-based indices) for GWR, we used permutation tests as a way for detecting statistical differences. LME was significantly better than the other models, as well as GWR was to OLS and GLS. Our results indicate that the LME model produced the best predictions of tree diameter from LiDAR-based variables to a degree that has previously not been possible.

@Article{Salas2010,
Title = {Modelling tree diameter from airborne laser scanning derived variables: A comparison of spatial statistical models},
Author = {Salas, Christian and Ene, Liviu and Gregoire, Timothy G. and Næsset, Erik and Gobakken, Terje},
Journal = {Remote Sensing of Environment},
Year = {2010},
Note = {doi: DOI: 10.1016/j.rse.2010.01.020},
Number = {6},
Pages = {1277-1285},
Volume = {114},
Abstract = {Formerly, tree height has been more difficult to measure accurately in the field than tree diameter at breast height. As a consequence, models to predict height from diameter measurements have been widely developed in the forestry literature. Through the use of airborne laser scanning technology (e.g., LiDAR), tree variables such as height and crown diameter can be measured accurately, a development which has spawned the need for models to predict diameter from airborne laser-derived measurements. Although some work has been done for fitting such models, none have incorporated spatial information to improve the accuracy of the predicted diameters. Using a simple linear model for predicting tree diameter from laser-derived tree height and crown diameter measurements, we compared the performance of ordinary least squares (OLS), generalized least squares with a non-null correlation structure (GLS), linear mixed-effects model (LME), and geographically weighted regression (GWR). Our data were obtained from 36 sample plots established in Norway. This is the first study to examine the use of spatial statistical models for tree-level LiDAR data. Root mean square prediction errors in tree diameter with LME are 3.5%, with GWR are 10%, and with OLS and GLS are 17%. LME also exhibited low variability in predicting performance across all the validation classes (based on laser-derived height). Giving the difficulties of using parametric statistical inference (such as maximum likelihood-based indices) for GWR, we used permutation tests as a way for detecting statistical differences. LME was significantly better than the other models, as well as GWR was to OLS and GLS. Our results indicate that the LME model produced the best predictions of tree diameter from LiDAR-based variables to a degree that has previously not been possible.},
Keywords = {LiDAR Spatial correlation Mixed-effects models Geographically weighted regression Diameter-height models},
Owner = {hanso},
Timestamp = {2011.11.17}
}

The primary aim of this study was to investigate the suitability of interferometric X-band SAR (InSAR) for inventory of boreal forest biomass. We investigated the relationship between SRTM X-band InSAR height and above-ground biomass in a study area in southern Norway. We generated biomass reference data for each SRTM pixel from a field inventory in combination with airborne laser scanning (ALS). One set of forest inventory plots served for calibrating ALS based biomass models, and another set of field plots was used to validate these models. The biomass values obtained in this way ranged up to 250 t/ha at the stand level. The relationship between biomass and InSAR height was linear, no apparent saturation effect was present, and the accuracy was high (RMSE = 19%). The relationship differed between Norway spruce and Scots pine, where an increase in InSAR height of 1 m corresponded to an increase in biomass of 9.9 and 7.0 t/ha, respectively. Using a high-quality terrain model from ALS enabled biomass to be estimated with a higher accuracy as compared to using a terrain model from topographic maps. Interferometric X-band SAR appears to be a promising method for forest biomass monitoring.

@Article{Solberg2010a,
Title = {Estimating spruce and pine biomass with interferometric X-band SAR},
Author = {Solberg, Svein and Astrup, Rasmus and Gobakken, Terje and Næsset, Erik and Weydahl, Dan J.},
Journal = {Remote Sensing of Environment},
Year = {2010},
Number = {10},
Pages = {2353-2360},
Volume = {114},
Abstract = {The primary aim of this study was to investigate the suitability of interferometric X-band SAR (InSAR) for inventory of boreal forest biomass. We investigated the relationship between SRTM X-band InSAR height and above-ground biomass in a study area in southern Norway. We generated biomass reference data for each SRTM pixel from a field inventory in combination with airborne laser scanning (ALS). One set of forest inventory plots served for calibrating ALS based biomass models, and another set of field plots was used to validate these models. The biomass values obtained in this way ranged up to 250 t/ha at the stand level. The relationship between biomass and InSAR height was linear, no apparent saturation effect was present, and the accuracy was high (RMSE = 19%). The relationship differed between Norway spruce and Scots pine, where an increase in InSAR height of 1 m corresponded to an increase in biomass of 9.9 and 7.0 t/ha, respectively. Using a high-quality terrain model from ALS enabled biomass to be estimated with a higher accuracy as compared to using a terrain model from topographic maps. Interferometric X-band SAR appears to be a promising method for forest biomass monitoring.},
Keywords = {SAR InSAR Biomass Forestry LIDAR},
Owner = {hanso},
Timestamp = {2011.11.17}
}

The objectives of this study were to quantify and analyze differences in laser height and laser intensity distributions of individual trees obtained from airborne laser scanner (ALS) data for different canopy conditions (leaf-on vs. leaf-off) and sensors. It was also assessed how estimated tree height, stem diameter, and tree species were influenced by these differences. The study was based on 412 trees from a boreal forest reserve in Norway. Three different ALS acquisitions were carried out. Leaf-on and leaf-off data were acquired with the Optech ALTM 3100 sensor, and an additional leaf-on dataset was acquired using the Optech ALTM 1233 sensor. Laser echoes located within the vertical projection of the tree crowns were attributed to different echo categories ("first echoes of many", "single echoes", "last echoes of many") and analyzed. The most pronounced changes in laser height distribution from leaf-on to leaf-off were found for the echo categories denoted as "single" and "last echoes of many" where the distributions were shifted towards the ground under leaf-off conditions. The most pronounced change in the intensity distribution was found for "first echoes of many" where the distribution was extremely skewed towards the lower values under leaf-off conditions compared to leaf-on. Furthermore, the echo height and intensity distributions obtained for the two different sensors also differed significantly. Individual tree properties were estimated fairly accurately in all acquisitions with RMSE ranging from 0.76 to 0.84 m for tree height and from 3.10 to 3.17 cm for stem diameter. It was revealed that tree species was an important model term in both and tree height and stem diameter models. A significantly higher overall accuracy of tree species classification was obtained using the leaf-off acquisition (90 vs. 98%) whereas classification accuracy did not differ much between sensors (90 vs. 93%).

@Article{Oerka2010,
Title = {Effects of different sensors and leaf-on and leaf-off canopy conditions on echo distributions and individual tree properties derived from airborne laser scanning},
Author = {Ørka, Hans Ole and Næsset, Erik and Bollandsås, Ole Martin},
Journal = {Remote Sensing of Environment},
Year = {2010},
Number = {7},
Pages = {1445-1461},
Volume = {114},
Abstract = {The objectives of this study were to quantify and analyze differences in laser height and laser intensity distributions of individual trees obtained from airborne laser scanner (ALS) data for different canopy conditions (leaf-on vs. leaf-off) and sensors. It was also assessed how estimated tree height, stem diameter, and tree species were influenced by these differences. The study was based on 412 trees from a boreal forest reserve in Norway. Three different ALS acquisitions were carried out. Leaf-on and leaf-off data were acquired with the Optech ALTM 3100 sensor, and an additional leaf-on dataset was acquired using the Optech ALTM 1233 sensor. Laser echoes located within the vertical projection of the tree crowns were attributed to different echo categories ("first echoes of many", "single echoes", "last echoes of many") and analyzed. The most pronounced changes in laser height distribution from leaf-on to leaf-off were found for the echo categories denoted as "single" and "last echoes of many" where the distributions were shifted towards the ground under leaf-off conditions. The most pronounced change in the intensity distribution was found for "first echoes of many" where the distribution was extremely skewed towards the lower values under leaf-off conditions compared to leaf-on. Furthermore, the echo height and intensity distributions obtained for the two different sensors also differed significantly. Individual tree properties were estimated fairly accurately in all acquisitions with RMSE ranging from 0.76 to 0.84 m for tree height and from 3.10 to 3.17 cm for stem diameter. It was revealed that tree species was an important model term in both and tree height and stem diameter models. A significantly higher overall accuracy of tree species classification was obtained using the leaf-off acquisition (90 vs. 98%) whereas classification accuracy did not differ much between sensors (90 vs. 93%).},
Keywords = {Airborne laser scanning Leaf-on canopy conditions Leaf-off canopy conditions, Sensors Individual trees Intensity Tree height Stem diameter Species classification},
Owner = {hanso},
Timestamp = {2011.11.17}
}

The boreal-alpine transition zone represents the gradient, or ecotone, from boreal forest to open alpine tundra. At present, resource inventories and/or systematic monitoring of mountainous areas are not commonly undertaken in many regions and countries. Current plot-wise national forest inventories typically focus on monitoring of the productive forests which are more valuable from an economic perspective. There is an increasing demand for information concerning high altitude forests and full capture of treed areas to support national and international biomass and carbon reporting. Further, impacts of climate change are expected to be most pronounced over this transition zone, leading to a need for methods for monitoring the status and dynamics of vegetation over the ecotone. We propose a method for integrating airborne laser scanner (ALS) data collected as a strip sample and ancillary information to delineate the boreal-alpine transition zone. In this study, the boreal-alpine transition zone is defined according to international definitions based on tree heights and crown coverage to provide the basis for reporting according to established international standards. The 3-dimensional measurements of forest structure obtained from an airborne laser scanner provided the basis for detecting the boreal-alpine transition zone. We establish, validate, and discuss an heuristic method to delineate the boreal-alpine transition zone using ALS data. The method was implemented using 53 ALS sample strips in Hedmark County, Norway, and validated with field measurements of the transition zone represented by forest and tree lines at 26 locations. The ALS delineation of the boreal-alpine transition was accurate when compared to field measurements. Furthermore, a non-parametric method was used to upscale the ALS estimates to the entire area of Hedmark County (27 400 km2) using Landsat images and information derived from a digital terrain model as ancillary data. The size of the estimated boreal-alpine transition zone in Hedmark was 3750 km2.

@InProceedings{Oerka2010a,
Title = {Integrating airborne laser scanner data and ancillary information for delineating the boreal-alpine transition zone in Hedmark County, Norway},
Author = {Ørka, Hans Ole and Wulder, Mike and Gobakken, Terje and Næsset, Erik},
Booktitle = {Proceedings of SilviLaser 2010, The 10th annual conference on lidar applications for assessing forest ecosystems. Freiburg, Germany.},
Year = {2010},
Abstract = {The boreal-alpine transition zone represents the gradient, or ecotone, from boreal forest to open alpine tundra. At present, resource inventories and/or systematic monitoring of mountainous areas are not commonly undertaken in many regions and countries. Current plot-wise national forest inventories typically focus on monitoring of the productive forests which are more valuable from an economic perspective. There is an increasing demand for information concerning high altitude forests and full capture of treed areas to support national and international biomass and carbon reporting. Further, impacts of climate change are expected to be most pronounced over this transition zone, leading to a need for methods for monitoring the status and dynamics of vegetation over the ecotone. We propose a method for integrating airborne laser scanner (ALS) data collected as a strip sample and ancillary information to delineate the boreal-alpine transition zone. In this study, the boreal-alpine transition zone is defined according to international definitions based on tree heights and crown coverage to provide the basis for reporting according to established international standards. The 3-dimensional measurements of forest structure obtained from an airborne laser scanner provided the basis for detecting the boreal-alpine transition zone. We establish, validate, and discuss an heuristic method to delineate the boreal-alpine transition zone using ALS data. The method was implemented using 53 ALS sample strips in Hedmark County, Norway, and validated with field measurements of the transition zone represented by forest and tree lines at 26 locations. The ALS delineation of the boreal-alpine transition was accurate when compared to field measurements. Furthermore, a non-parametric method was used to upscale the ALS estimates to the entire area of Hedmark County (27 400 km2) using Landsat images and information derived from a digital terrain model as ancillary data. The size of the estimated boreal-alpine transition zone in Hedmark was 3750 km2.},
Keywords = {airborne laser scanning satellite images boreal-alpine transiton zone},
Owner = {hanso},
Timestamp = {2011.11.17}
}

Distance-independent, non-linear models of 5-year diameter increment for Norway spruce, Scots pine, birch and other broadleaved trees were developed, evaluated and compared on data from the Norwegian National Forest Inventory. The models were based on a Weibull curve dependent on diameter and a modifier function dependent on site parameters. A non-linear mixed model approach was used to fit the models. Model fit was assessed by the <i>Pseudo</i>-<i>R</i><sup>2</sup> on total level and on plot level reflecting a random plot effect. The <a name="ILM0001">Ã‚ </a> values were 0.31, 0.20, 0.13 and 0.10 for Norway spruce, Scots pine, birch and other broadleaves, respectively. The bias estimates from an independent validation were 0.1, 0.0, 0.1 and 0.3 mm for Norway spruce, Scots pine, birch and other broadleaves, respectively. The results from the independent validation were compared with results from identical tests of three other sets of models. The comparison indicated good model performance of the current models. The models were also compared by evaluating their behaviour when the tree size component was extrapolated beyond the range of the model development data. The results indicated that an additive model of diameter increment and the current non-linear Weibull and modifier model of diameter increment performed best.

@Article{Bollandsaas2009,
Title = {Weibull models for single-tree increment of Norway spruce, Scots pine, birch and other broadleaves in Norway},
Author = {Bollandsås, Ole Martin and Næsset, Erik},
Journal = {Scandinavian Journal of Forest Research},
Year = {2009},
Number = {1},
Pages = {54 - 66},
Volume = {24},
Abstract = {Distance-independent, non-linear models of 5-year diameter increment for Norway spruce, Scots pine, birch and other broadleaved trees were developed, evaluated and compared on data from the Norwegian National Forest Inventory. The models were based on a Weibull curve dependent on diameter and a modifier function dependent on site parameters. A non-linear mixed model approach was used to fit the models. Model fit was assessed by the <i>Pseudo</i>-<i>R</i><sup>2</sup> on total level and on plot level reflecting a random plot effect. The <a name="ILM0001">Ã‚ </a> values were 0.31, 0.20, 0.13 and 0.10 for Norway spruce, Scots pine, birch and other broadleaves, respectively. The bias estimates from an independent validation were 0.1, 0.0, 0.1 and 0.3 mm for Norway spruce, Scots pine, birch and other broadleaves, respectively. The results from the independent validation were compared with results from identical tests of three other sets of models. The comparison indicated good model performance of the current models. The models were also compared by evaluating their behaviour when the tree size component was extrapolated beyond the range of the model development data. The results indicated that an additive model of diameter increment and the current non-linear Weibull and modifier model of diameter increment performed best.},
Owner = {hanso},
Timestamp = {2011.11.17}
}

The objectives of this study were (1) to develop models for estimation of total above-ground biomass, tree crown biomass and stem biomass of mountain birch (<i>Betula pubescens</i> spp. <i>czerepanÃƒÂ³vii</i>), and (2) to test the stability of the relationships between biomass and biophysical tree properties across geographical regions and tree size ranges. The models were developed using a mixed modelling approach accounting for the hierarchical structure of the data that originated from sample plots. Diameter at breast height, tree height, and the ratio between height and diameter were candidate explanatory variables, but only diameter was statistically significant (<i>p</i>&lt;0.05). The model fit values (pseudo-<i>R</i> <sup>2</sup>) were 0.91, 0.60 and 0.85 for the three respective models. A substantial part of the model random errors could be attributed to between-plot variations. The conclusion related to objective (1) was that the models are well suited for biomass prediction of mountain birch in the mountain areas of southern Norway. Furthermore, models reported in previous research that had been calibrated on data from other regions were applied on the current data set. The results indicate that models calibrated for small trees produced predictions diverging from the observed values of the current data set. The differences between predicted and observed values also seem to vary along a site productivity gradient. Still, even though the differences between predicted and observed values using the different models varied quite a lot, the relationships were relatively stable within certain limits. The conclusion related to objective (2) was that biomass models can be applied outside the region for which they were developed, which in many cases is necessary because local models do not exist. However, the properties of the model development data related to tree size range and site productivity should be similar to those of the area for which predictions are being made.

@Article{Bollandsaas2009a,
Title = {Models for predicting above-ground biomass of Betula pubescens spp. czerepanÃ³vii in mountain areas of southern Norway},
Author = {Bollandsås, Ole Martin and Rekstad, Ingvild and Næsset, Erik and Røsberg, Ingvald},
Journal = {Scandinavian Journal of Forest Research},
Year = {2009},
Number = {4},
Pages = {318 - 332},
Volume = {24},
Abstract = {The objectives of this study were (1) to develop models for estimation of total above-ground biomass, tree crown biomass and stem biomass of mountain birch (<i>Betula pubescens</i> spp. <i>czerepanÃƒÂ³vii</i>), and (2) to test the stability of the relationships between biomass and biophysical tree properties across geographical regions and tree size ranges. The models were developed using a mixed modelling approach accounting for the hierarchical structure of the data that originated from sample plots. Diameter at breast height, tree height, and the ratio between height and diameter were candidate explanatory variables, but only diameter was statistically significant (<i>p</i>&lt;0.05). The model fit values (pseudo-<i>R</i> <sup>2</sup>) were 0.91, 0.60 and 0.85 for the three respective models. A substantial part of the model random errors could be attributed to between-plot variations. The conclusion related to objective (1) was that the models are well suited for biomass prediction of mountain birch in the mountain areas of southern Norway. Furthermore, models reported in previous research that had been calibrated on data from other regions were applied on the current data set. The results indicate that models calibrated for small trees produced predictions diverging from the observed values of the current data set. The differences between predicted and observed values also seem to vary along a site productivity gradient. Still, even though the differences between predicted and observed values using the different models varied quite a lot, the relationships were relatively stable within certain limits. The conclusion related to objective (2) was that biomass models can be applied outside the region for which they were developed, which in many cases is necessary because local models do not exist. However, the properties of the model development data related to tree size range and site productivity should be similar to those of the area for which predictions are being made.},
Owner = {hanso},
Timestamp = {2011.11.17}
}

Spatiotemporal data from satellite remote sensing and surface meteorology networks have made it possible to continuously monitor global plant production, and to identify global trends associated with land cover/use and climate change. Gross primary production (GPP) and net primary production (NPP) are routinely derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard satellites Terra and Aqua, and estimates generally agree with independent measurements at validation sites across the globe. However, the accuracy of GPP and NPP estimates in some regions may be limited by the quality of model input variables and heterogeneity at fine spatial scales. We developed new methods for deriving model inputs (i.e., land cover, leaf area, and photosynthetically active radiation absorbed by plant canopies) from airborne laser altimetry (LiDAR) and Quickbird multispectral data at resolutions ranging from about 30 m to 1 km. In addition, LiDAR-derived biomass was used as a means for computing carbon-use efficiency. Spatial variables were used with temporal data from ground-based monitoring stations to compute a six-year GPP and NPP time series for a 3600 ha study site in the Great Lakes region of North America. Model results compared favorably with independent observations from a 400 m flux tower and a process-based ecosystem model (BIOME-BGC), but only after removing vapor pressure deficit as a constraint on photosynthesis from the MODIS global algorithm. Fine-resolution inputs captured more of the spatial variability, but estimates were similar to coarse-resolution data when integrated across the entire landscape. Failure to account for wetlands had little impact on landscape-scale estimates, because vegetation structure, composition, and conversion efficiencies were similar to upland plant communities. Plant productivity estimates were noticeably improved using LiDAR-derived variables, while uncertainties associated with land cover generalizations and wetlands in this largely forested landscape were considered less important.

@Article{Cook2009,
Title = {Using LiDAR and quickbird data to model plant production and quantify uncertainties associated with wetland detection and land cover generalizations},
Author = {Cook, Bruce D. and Bolstad, Paul V. and Næsset, Erik and Anderson, Ryan S. and Garrigues, Sebastian and Morisette, Jeffrey T. and Nickeson, Jaime and Davis, Kenneth J.},
Journal = {Remote Sensing of Environment},
Year = {2009},
Note = {doi: DOI: 10.1016/j.rse.2009.06.017},
Number = {11},
Pages = {2366-2379},
Volume = {113},
Abstract = {Spatiotemporal data from satellite remote sensing and surface meteorology networks have made it possible to continuously monitor global plant production, and to identify global trends associated with land cover/use and climate change. Gross primary production (GPP) and net primary production (NPP) are routinely derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard satellites Terra and Aqua, and estimates generally agree with independent measurements at validation sites across the globe. However, the accuracy of GPP and NPP estimates in some regions may be limited by the quality of model input variables and heterogeneity at fine spatial scales. We developed new methods for deriving model inputs (i.e., land cover, leaf area, and photosynthetically active radiation absorbed by plant canopies) from airborne laser altimetry (LiDAR) and Quickbird multispectral data at resolutions ranging from about 30 m to 1 km. In addition, LiDAR-derived biomass was used as a means for computing carbon-use efficiency. Spatial variables were used with temporal data from ground-based monitoring stations to compute a six-year GPP and NPP time series for a 3600 ha study site in the Great Lakes region of North America. Model results compared favorably with independent observations from a 400 m flux tower and a process-based ecosystem model (BIOME-BGC), but only after removing vapor pressure deficit as a constraint on photosynthesis from the MODIS global algorithm. Fine-resolution inputs captured more of the spatial variability, but estimates were similar to coarse-resolution data when integrated across the entire landscape. Failure to account for wetlands had little impact on landscape-scale estimates, because vegetation structure, composition, and conversion efficiencies were similar to upland plant communities. Plant productivity estimates were noticeably improved using LiDAR-derived variables, while uncertainties associated with land cover generalizations and wetlands in this largely forested landscape were considered less important.},
Doi = {10.1016/j.rse.2009.06.017},
Keywords = {Primary production Leaf area index (LAI) Light-use efficiency Carbon-use efficiency Moderate Resolution Imaging Spectroradiometer (MODIS) Digital hemispheric photography Eddy covariance},
Owner = {hanso},
Timestamp = {2011.11.17},
Url = {http://www.sciencedirect.com/science/article/pii/S0034425709002119}
}

LiDAR data are increasingly available from both airborne and spaceborne missions to map elevation and vegetation structure. Additionally, global coverage may soon become available with NASA’s planned DESDynI sensor. However, substantial challenges remain to using the growing body of LiDAR data. First, the large volumes of data generated by LiDAR sensors require efficient processing methods. Second, efficient sampling methods are needed to collect the field data used to relate LiDAR data with vegetation structure. In this paper, we used low-density LiDAR data, summarized within pixels of a regular grid, to estimate forest structure and biomass across a 53,600 ha study area in northeastern Wisconsin. Additionally, we compared the predictive ability of models constructed from a random sample to a sample stratified using mean and standard deviation of LiDAR heights. Our models explained between 65 to 88% of the variability in DBH, basal area, tree height, and biomass. Prediction errors from models constructed using a random sample were up to 68% larger than those from the models built with a stratified sample. The stratified sample included a greater range of variability than the random sample. Thus, applying the random sample model to the entire population violated a tenet of regression analysis; namely, that models should not be used to extrapolate beyond the range of data from which they were constructed. Our results highlight that LiDAR data integrated with field data sampling designs can provide broad-scale assessments of vegetation structure and biomass, i.e., information crucial for carbon and biodiversity science.

@Article{Hawbaker2009,
Title = {Improved estimates of forest vegetation structure and biomass with a LiDAR-optimized sampling design},
Author = {Hawbaker, Todd J. and Keuler, Nicholas S. and Lesak, Adrian A. and Gobakken, Terje and Contrucci, Kirk and Radeloff, Volker C.},
Journal = {Journal of Geophysical Research},
Year = {2009},
Volume = {114},
Abstract = {LiDAR data are increasingly available from both airborne and spaceborne missions to map elevation and vegetation structure. Additionally, global coverage may soon become available with NASA's planned DESDynI sensor. However, substantial challenges remain to using the growing body of LiDAR data. First, the large volumes of data generated by LiDAR sensors require efficient processing methods. Second, efficient sampling methods are needed to collect the field data used to relate LiDAR data with vegetation structure. In this paper, we used low-density LiDAR data, summarized within pixels of a regular grid, to estimate forest structure and biomass across a 53,600 ha study area in northeastern Wisconsin. Additionally, we compared the predictive ability of models constructed from a random sample to a sample stratified using mean and standard deviation of LiDAR heights. Our models explained between 65 to 88% of the variability in DBH, basal area, tree height, and biomass. Prediction errors from models constructed using a random sample were up to 68% larger than those from the models built with a stratified sample. The stratified sample included a greater range of variability than the random sample. Thus, applying the random sample model to the entire population violated a tenet of regression analysis; namely, that models should not be used to extrapolate beyond the range of data from which they were constructed. Our results highlight that LiDAR data integrated with field data sampling designs can provide broad-scale assessments of vegetation structure and biomass, i.e., information crucial for carbon and biodiversity science.},
Keywords = {airborne laser scanning sample design DESDynI tree height diameter at breast height (DBH) basal area 0480 Biogeosciences: Remote sensing 0439 Biogeosciences: Ecosystems, structure and dynamics 0430 Biogeosciences: Computational methods and data processing 0410 Biogeosciences: Biodiversity 0428 Biogeosciences: Carbon cycling},
Owner = {hanso},
Timestamp = {2011.11.17}
}

In forest inventories, the species information is crucial for economical, ecological and technical reasons. Species recognition is currently a bottleneck in practical remote sensing applications. Here, we examined species discrimination using tree-level LiDAR features in discrete-return data. The aim was to examine the robustness and explanatory power of the intensity and height distribution features. A dataset consisting of 13890 trees from 117 stands in southern Finland (61Â°50’N, 24Â°20’E) was used. The data of two LiDAR sensors was fused using intensity normalization in natural targets. Age dependency of first-return intensity was observed in spruce and birch trees, which needs to be considered in using LiDAR intensity metrics. Classification of Scots pine, Norway spruce and birch was tested and accuracy was 81?85%. Separation of pine and spruce was more accurate, 91?93%. We also present results for 15 rare conifer and broadleaved species. To enhance the classification accuracy of birch, we propose co-use of image features.

@InProceedings{Korpela2009,
Title = {Small-footprint discrete-return LiDAR in tree species recognition},
Author = {Korpela, I. and Tokola, T. and Ørka, H.O. and Koskinen, M.},
Year = {2009},
Month = {June 2 - 5, 2009},
Abstract = {In forest inventories, the species information is crucial for economical, ecological and technical reasons. Species recognition is currently a bottleneck in practical remote sensing applications. Here, we examined species discrimination using tree-level LiDAR features in discrete-return data. The aim was to examine the robustness and explanatory power of the intensity and height distribution features. A dataset consisting of 13890 trees from 117 stands in southern Finland (61Â°50'N, 24Â°20'E) was used. The data of two LiDAR sensors was fused using intensity normalization in natural targets. Age dependency of first-return intensity was observed in spruce and birch trees, which needs to be considered in using LiDAR intensity metrics. Classification of Scots pine, Norway spruce and birch was tested and accuracy was 81?85%. Separation of pine and spruce was more accurate, 91?93%. We also present results for 15 rare conifer and broadleaved species. To enhance the classification accuracy of birch, we propose co-use of image features.},
Owner = {hanso},
Timestamp = {2011.11.17}
}

The aim of this study was to apply the non-parametric k-most similar neighbour (MSN) method and airborne laser scanner data to predict stand diameter distributions in a 960 km2 forest district in south-eastern Norway. The specific objectives of the study were (1) to examine the use of different dependent and independent variables in the canonical correlation analysis of MSN, and (2) to examine the influence of reduced number of training data plots by means of simulations. The reliability of the constructed diameter distributions was analysed using error indices and the accuracy of stand attributes derived from predicted diameter distributions. The study material included a total of 201 plots and they were reduced to 181, 161, … , 41 plots in the simulations. The results indicated that when selecting dependent variables in the canonical correlation analysis it is sufficient to have variables reflecting stand means and aggregated variables (sums) to obtain accurate predictions of diameter distributions. Furthermore, the prediction models should not to be too detailed, i.e. they should not include a great number of independent variables since cross-validation always tends to give too optimistic results. Validation on independent data will often show considerably poorer reliability figures. Finally, the results indicated that even such a low number of training plots as about 100 can produce accurate enough predictions of stand attributes and diameter distributions.

@Article{Maltamo2009a,
Title = {Non-parametric prediction of diameter distributions using airborne laser scanner data},
Author = {Maltamo, M. and Næsset, E. and Bollandsås, O. M. and Gobakken, T. and Packalén, P.},
Journal = {Scandinavian Journal of Forest Research},
Year = {2009},
Note = {Maltamo, Matti Naesset, Erik Bollandsas, Ole M. Gobakken, Terje Packalen, Petteri},
Number = {6},
Pages = {541-553},
Volume = {24},
Abstract = {The aim of this study was to apply the non-parametric k-most similar neighbour (MSN) method and airborne laser scanner data to predict stand diameter distributions in a 960 km2 forest district in south-eastern Norway. The specific objectives of the study were (1) to examine the use of different dependent and independent variables in the canonical correlation analysis of MSN, and (2) to examine the influence of reduced number of training data plots by means of simulations. The reliability of the constructed diameter distributions was analysed using error indices and the accuracy of stand attributes derived from predicted diameter distributions. The study material included a total of 201 plots and they were reduced to 181, 161, ... , 41 plots in the simulations. The results indicated that when selecting dependent variables in the canonical correlation analysis it is sufficient to have variables reflecting stand means and aggregated variables (sums) to obtain accurate predictions of diameter distributions. Furthermore, the prediction models should not to be too detailed, i.e. they should not include a great number of independent variables since cross-validation always tends to give too optimistic results. Validation on independent data will often show considerably poorer reliability figures. Finally, the results indicated that even such a low number of training plots as about 100 can produce accurate enough predictions of stand attributes and diameter distributions.},
Owner = {hanso},
Timestamp = {2011.11.17}
}

Canopy height distributions were created from small-footprint airborne laser scanner (ALS) data collected over 40 field sample plots with size 1000 m2 located in mature conifer forest. ALS data were collected with two different instruments, i.e., the ALTM 1233 and ALTM 3100 laser scanners (Optech Inc.). The ALTM 1233 data were acquired at a flying altitude of 1200 m and a pulse repetition frequency (PRF) of 33 kHz. Three different acquisitions were carried out with ALTM 3100, i.e., (1) a flying altitude of 1100 m and a PRF of 50 kHz, (2) a flying altitude of 1100 m and a PRF of 100 kHz, and (3) a flying altitude of 2000 m and a PRF of 50 kHz. Height percentiles, mean and maximum height values, coefficients of variation of the heights, and canopy density at different height intervals above the ground were derived from the four different ALS datasets and for single + first and last echoes of the ALS data separately. The ALS-derived height- and density variables were assessed in pair-wise comparisons to evaluate the effects of (a) instrument, (b) flying altitude, and (c) PRF. A systematic shift in height values of up to 0.3 m between sensors when the first echoes were compared was demonstrated. Also the density-related variables differed significantly between the two instruments. Comparisons of flying altitudes and PRFs revealed upwards shifted canopy height distributions for the highest flying altitude (2000 m) and the lowest PRF (50 kHz). The distribution of echoes on different echo categories, i.e., single and multiple (first and last) echoes, differed significantly between acquisitions. The proportion of multiple echoes decreased with increasing flying altitude and PRF. Different echo categories have different properties since it is likely that single echoes tend to occur in the densest parts of the tree crowns, i.e., near the apex where the concentration of biological matter is highest and distance to the ground is largest. To assess the influence of instrument, flying altitude, and PRF on biophysical properties derived from ALS data, regression analysis was carried out to relate ALS-derived metrics to mean tree height (hL) and timber volume (V). Cross validation revealed only minor differences in precision for the different ALS acquisitions, but systematic differences between acquisitions of up to 2.5% for hL and 10.7% for V were found when comparing data from different acquisitions.

@Article{Naesset2009,
Title = {Effects of different sensors, flying altitudes, and pulse repetition frequencies on forest canopy metrics and biophysical stand properties derived from small-footprint airborne laser data},
Author = {Næsset, Erik},
Journal = {Remote Sensing of Environment},
Year = {2009},
Note = {doi: DOI: 10.1016/j.rse.2008.09.001},
Number = {1},
Pages = {148-159},
Volume = {113},
Abstract = {Canopy height distributions were created from small-footprint airborne laser scanner (ALS) data collected over 40 field sample plots with size 1000 m2 located in mature conifer forest. ALS data were collected with two different instruments, i.e., the ALTM 1233 and ALTM 3100 laser scanners (Optech Inc.). The ALTM 1233 data were acquired at a flying altitude of 1200 m and a pulse repetition frequency (PRF) of 33 kHz. Three different acquisitions were carried out with ALTM 3100, i.e., (1) a flying altitude of 1100 m and a PRF of 50 kHz, (2) a flying altitude of 1100 m and a PRF of 100 kHz, and (3) a flying altitude of 2000 m and a PRF of 50 kHz. Height percentiles, mean and maximum height values, coefficients of variation of the heights, and canopy density at different height intervals above the ground were derived from the four different ALS datasets and for single + first and last echoes of the ALS data separately. The ALS-derived height- and density variables were assessed in pair-wise comparisons to evaluate the effects of (a) instrument, (b) flying altitude, and (c) PRF. A systematic shift in height values of up to 0.3 m between sensors when the first echoes were compared was demonstrated. Also the density-related variables differed significantly between the two instruments. Comparisons of flying altitudes and PRFs revealed upwards shifted canopy height distributions for the highest flying altitude (2000 m) and the lowest PRF (50 kHz). The distribution of echoes on different echo categories, i.e., single and multiple (first and last) echoes, differed significantly between acquisitions. The proportion of multiple echoes decreased with increasing flying altitude and PRF. Different echo categories have different properties since it is likely that single echoes tend to occur in the densest parts of the tree crowns, i.e., near the apex where the concentration of biological matter is highest and distance to the ground is largest. To assess the influence of instrument, flying altitude, and PRF on biophysical properties derived from ALS data, regression analysis was carried out to relate ALS-derived metrics to mean tree height (hL) and timber volume (V). Cross validation revealed only minor differences in precision for the different ALS acquisitions, but systematic differences between acquisitions of up to 2.5% for hL and 10.7% for V were found when comparing data from different acquisitions.},
Keywords = {Forest inventory Laser scanning Canopy height Canopy density},
Owner = {hanso},
Timestamp = {2011.11.17}
}

It has been suggested that airborne laser scanning (ALS) with high point densities could be used to monitor changes in the alpine tree line. The overall goal of this study was to assess the influence of ALS sensor and flight configurations on the ability to detect small trees in the alpine tree line and on the estimation of their heights. The study was conducted in a sub-alpine/alpine environment in southeast Norway. 342 small trees (0.11-5.20 m tall) of Norway spruce, Scots pine, and downy birch were precisely georeferenced and measured in field. ALS data acquired with two different instruments and at different flying altitudes (700-1130 m a.g.l.) with different pulse repetition frequencies (100, 125, and 166 kHz) were collected with a point density of all echoes of 7.7-11.0 m(-2). For each acquisition, three different terrain models were used to process the ALS point clouds in order to assess the effects of different preprocessing parameters on the ability to detect small trees. Regardless of acquisition and terrain model, positive height values were found for 91% of the taller trees (>1 m). For smaller trees (<1 m), 29-61% of the trees displayed positive height values. For the lowest repetition frequencies (100 and 125 kHz) in particular, the portion of trees with positive laser height values increased significantly with increasing terrain smoothing. For the highest repetition frequency there were no differences between smoothing levels, likely because of large ALS measurement errors at low laser pulse energy levels causing a large portion of the laser echoes to be discarded during terrain modeling. Error analysis revealed large commission errors when detecting small trees. The commissions consisted of objects like terrain structures, rocks, and hummocks having positive height values. The magnitude of commissions ranged from 709 to 8948% of the true tree numbers and tended to increase with increasing levels of terrain smoothing and with acquisitions according to increasing point densities. The accuracy of tree height derived from the ALS data indicated a systematic underestimation of true tree height by 0.35 to 1.47 m. depending on acquisition, terrain model, and tree species. The underestimation also increased with increasing tree height. The standard deviation for the differences between laser-derived and field-measured tree heights was 0.16-0.57 m. Because there are significant effects of sensor and flight configurations on tree height estimation, field calibration of tree heights at each point of time is required when using airborne lasers for tree growth monitoring. (C) 2009 Elsevier Inc. All rights reserved.

@Article{Naesset2009b,
Title = {Influence of terrain model smoothing and flight and sensor configurations on detection of small pioneer trees in the boreal-alpine transition zone utilizing height metrics derived from airborne scanning lasers},
Author = {Næsset, E.},
Journal = {Remote Sensing of Environment},
Year = {2009},
Note = {Naesset, Erik},
Number = {10},
Pages = {2210-2223},
Volume = {113},
Abstract = {It has been suggested that airborne laser scanning (ALS) with high point densities could be used to monitor changes in the alpine tree line. The overall goal of this study was to assess the influence of ALS sensor and flight configurations on the ability to detect small trees in the alpine tree line and on the estimation of their heights. The study was conducted in a sub-alpine/alpine environment in southeast Norway. 342 small trees (0.11-5.20 m tall) of Norway spruce, Scots pine, and downy birch were precisely georeferenced and measured in field. ALS data acquired with two different instruments and at different flying altitudes (700-1130 m a.g.l.) with different pulse repetition frequencies (100, 125, and 166 kHz) were collected with a point density of all echoes of 7.7-11.0 m(-2). For each acquisition, three different terrain models were used to process the ALS point clouds in order to assess the effects of different preprocessing parameters on the ability to detect small trees. Regardless of acquisition and terrain model, positive height values were found for 91% of the taller trees (>1 m). For smaller trees (<1 m), 29-61% of the trees displayed positive height values. For the lowest repetition frequencies (100 and 125 kHz) in particular, the portion of trees with positive laser height values increased significantly with increasing terrain smoothing. For the highest repetition frequency there were no differences between smoothing levels, likely because of large ALS measurement errors at low laser pulse energy levels causing a large portion of the laser echoes to be discarded during terrain modeling. Error analysis revealed large commission errors when detecting small trees. The commissions consisted of objects like terrain structures, rocks, and hummocks having positive height values. The magnitude of commissions ranged from 709 to 8948% of the true tree numbers and tended to increase with increasing levels of terrain smoothing and with acquisitions according to increasing point densities. The accuracy of tree height derived from the ALS data indicated a systematic underestimation of true tree height by 0.35 to 1.47 m. depending on acquisition, terrain model, and tree species. The underestimation also increased with increasing tree height. The standard deviation for the differences between laser-derived and field-measured tree heights was 0.16-0.57 m. Because there are significant effects of sensor and flight configurations on tree height estimation, field calibration of tree heights at each point of time is required when using airborne lasers for tree growth monitoring. (C) 2009 Elsevier Inc. All rights reserved.},
Doi = {10.1016/j.rse.2009.06.003},
Owner = {hanso},
Timestamp = {2011.11.17},
Url = {http://www.sciencedirect.com/science/article/pii/S0034425709001825}
}

In this study we demonstrate how airborne laser scanning (ALS) can be applied to map effective leaf area index (LAIe) in a spruce forest, after being calibrated with ground based measurements. In 2003 and 2005, ALS data and field estimates of LAIe were acquired in a Norway spruce forest in SE Norway. We used LI-COR’s LAI-2000Â® Plant canopy analyzer ("LAI-2000") and hemispherical images ("HI") for field based estimates of LAIe. ALS penetration rate calculated from first echoes and from first and last echoes was strongly related to field estimates of LAIe. We fitted regression models of LAIe against the log-transformed inverse of the ALS penetration rate, and in accordance with the Beer-Lambert law this produced a linear, no-intercept relationship. This was particularly the case for the LAI-2000, having R2 values > 0.9. The strongest relationship was obtained by selecting ALS data from within a circle around each plot with a radius of 0.75 times the tree height. We found a slight difference in the relationship for the two years, which can be attributed to the differences in the ALS acquisition settings. The relationship was valid across four age classes of trees representing different stages of stand development, except in one case with newly regenerated stands which most likely was an artifact. Using LAIe based on HI data produced weaker relationships with the ALS data. This was the case even when we simulated LAI-2000 measurements based on the HI data.

@Article{Solberg2009,
Title = {Mapping LAI in a Norway spruce forest using airborne laser scanning},
Author = {Solberg, Svein and Brunner, Andreas and Hanssen, Kjersti Holt and Lange, Holger and Næsset, Erik and Rautiainen, Miina and Stenberg, Pauline},
Journal = {Remote Sensing of Environment},
Year = {2009},
Note = {doi: DOI: 10.1016/j.rse.2009.06.010},
Number = {11},
Pages = {2317-2327},
Volume = {113},
Abstract = {In this study we demonstrate how airborne laser scanning (ALS) can be applied to map effective leaf area index (LAIe) in a spruce forest, after being calibrated with ground based measurements. In 2003 and 2005, ALS data and field estimates of LAIe were acquired in a Norway spruce forest in SE Norway. We used LI-COR's LAI-2000Â® Plant canopy analyzer ("LAI-2000") and hemispherical images ("HI") for field based estimates of LAIe. ALS penetration rate calculated from first echoes and from first and last echoes was strongly related to field estimates of LAIe. We fitted regression models of LAIe against the log-transformed inverse of the ALS penetration rate, and in accordance with the Beer-Lambert law this produced a linear, no-intercept relationship. This was particularly the case for the LAI-2000, having R2 values > 0.9. The strongest relationship was obtained by selecting ALS data from within a circle around each plot with a radius of 0.75 times the tree height. We found a slight difference in the relationship for the two years, which can be attributed to the differences in the ALS acquisition settings. The relationship was valid across four age classes of trees representing different stages of stand development, except in one case with newly regenerated stands which most likely was an artifact. Using LAIe based on HI data produced weaker relationships with the ALS data. This was the case even when we simulated LAI-2000 measurements based on the HI data.},
Doi = {10.1016/j.rse.2009.06.010},
Keywords = {LAI LIDAR Norway spruce},
Owner = {hanso},
Timestamp = {2011.11.17},
Url = {http://www.sciencedirect.com/science/article/pii/S0034425709001862}
}

The objective of this study was to predict the growth of forest stands of mixed tree species and size with natural recruitment. The stand state was defined by the number of spruce, pine, birch and other broadleaved trees by hectare in 15 diameter classes from 50 to 750 mm. The change in stand state over 5 years was predicted with state-dependent matrices based on equations for recruitment, growth and mortality. The data came from 7241 plots of the National Forest Inventory of Norway, measured from 1994 to 2005. A short-term validation was carried out by comparing predicted and actual growth over 10 years on 416 plots not used in model estimation. The model was also used to predict the long-term growth of stands with different initial species composition and diameter distribution. Irrespective of the initial condition the same steady state resulted, with characteristics similar to those observed in stands that had been undisturbed for 75 years.

@Article{Bollandsaas2008a,
Title = {Predicting the growth of stands of trees of mixed species and size: A matrix model for Norway},
Author = {Bollandsås, Ole Martin and Buongiorno, Joseph and Gobakken, Terje},
Journal = {Scandinavian Journal of Forest Research},
Year = {2008},
Number = {2},
Pages = {167 - 178},
Volume = {23},
Abstract = {The objective of this study was to predict the growth of forest stands of mixed tree species and size with natural recruitment. The stand state was defined by the number of spruce, pine, birch and other broadleaved trees by hectare in 15 diameter classes from 50 to 750 mm. The change in stand state over 5 years was predicted with state-dependent matrices based on equations for recruitment, growth and mortality. The data came from 7241 plots of the National Forest Inventory of Norway, measured from 1994 to 2005. A short-term validation was carried out by comparing predicted and actual growth over 10 years on 416 plots not used in model estimation. The model was also used to predict the long-term growth of stands with different initial species composition and diameter distribution. Irrespective of the initial condition the same steady state resulted, with characteristics similar to those observed in stands that had been undisturbed for 75 years.},
Owner = {hanso},
Timestamp = {2011.11.17}
}

The relationships between measures of forest structure as derived from airborne laser scanner data and the variation in quantity (Q) and vitality (V) of young trees in a size-diverse spruce forest were analyzed. A regeneration success rate (Q), leader length (V), relative leader length (V), and apical dominance ratio (V) were regressed against 27 different laser-derived explanatory variables representing three different spatial scales. The resulting 81 different models for each response variable were ranked according to their Akaike information criterion score and significance level. Each laser variable was then associated with four categories. These were scale, return, fraction, and type. Within the scale category, laser variables were grouped according to the spatial scale from which they originated. Similarly, within the return, fraction, and type categories, the variables were grouped according to if they originated from first or last return echoes; if they originated from lower, middle, or upper fraction of the range of laser heights or values derived from the full range of laser pulses, and if they were canopy height or canopy density metrics. The results show that the laser variables were strongest correlated with the quantity of small trees and that these variables could be attributed to large-scale, last return, lower fraction, and density metrics. The correlations with the vitality responses were weaker, but the results indicate that variables derived from a smaller scale than for the quantity were better in order to explain variation in leader length, relative leader length, and apical dominance ratio.

@Article{Bollandsaas2008,
Title = {Measures of spatial forest structure derived from airborne laser data are associated with natural regeneration patterns in an uneven-aged spruce forest},
Author = {Bollandsås, Ole Martin and Hanssen, Kjersti Holt and Marthiniussen, Solfrid and Næsset, Erik},
Journal = {Forest ecology and management},
Year = {2008},
Number = {3-4},
Pages = {953-961},
Volume = {255},
Abstract = {The relationships between measures of forest structure as derived from airborne laser scanner data and the variation in quantity (Q) and vitality (V) of young trees in a size-diverse spruce forest were analyzed. A regeneration success rate (Q), leader length (V), relative leader length (V), and apical dominance ratio (V) were regressed against 27 different laser-derived explanatory variables representing three different spatial scales. The resulting 81 different models for each response variable were ranked according to their Akaike information criterion score and significance level. Each laser variable was then associated with four categories. These were scale, return, fraction, and type. Within the scale category, laser variables were grouped according to the spatial scale from which they originated. Similarly, within the return, fraction, and type categories, the variables were grouped according to if they originated from first or last return echoes; if they originated from lower, middle, or upper fraction of the range of laser heights or values derived from the full range of laser pulses, and if they were canopy height or canopy density metrics. The results show that the laser variables were strongest correlated with the quantity of small trees and that these variables could be attributed to large-scale, last return, lower fraction, and density metrics. The correlations with the vitality responses were weaker, but the results indicate that variables derived from a smaller scale than for the quantity were better in order to explain variation in leader length, relative leader length, and apical dominance ratio.},
Doi = {10.1016/j.foreco.2007.10.017},
Keywords = {Regeneration Uneven-aged forest Laser scanner data Canopy structure},
Owner = {hanso},
Timestamp = {2011.11.17},
Url = {http://www.sciencedirect.com/science/article/pii/S0378112707007840}
}

Canopy height distributions were created from small-footprint airborne laser scanner data with an average sampling density of 1.13 points center dot m(-2) collected over 132 sample plots and 61 forest stands. Field measurements of each plot were carried out within two concentric circles corresponding to fixed areas of 200 m(2) and 300 or 400 m(2). The laser point clouds were thinned to approximately 0.25, 0.13, and 0.06 point center dot m(-2). For all comparisons, the maximum values of the first as well as last return canopy height distributions differed significantly between the full density and the thinned data. The combined effects of number of field plots, field plot sizes, and point densities on the accuracy of mean tree height, stand basal area, and stand volume predicted at stand level using a two-stage procedure combining field training data and laser data, were assessed using Monte Carlo simulation randomly selecting 75% and 50% of the field plots. The average standard deviation showed only a minor increase by decreasing point density and increased when the number of sample plots was reduced. The effects of field plot size varied with canopy structure and stem density.

@Article{Gobakken2008a,
Title = {Assessing effects of laser point density, ground sampling intensity, and field sample plot size on biophysical stand properties derived from airborne laser scanner data},
Author = {Gobakken, T. and Næsset, E.},
Journal = {Canadian Journal of Forest Research-Revue Canadienne De Recherche Forestiere},
Year = {2008},
Note = {ISI Document Delivery No.: 304PC Times Cited: 12 Cited Reference Count: 33 Gobakken, Terje Naesset, Erik NATL RESEARCH COUNCIL CANADA-N R C RESEARCH PRESS OTTAWA},
Number = {5},
Pages = {1095-1109},
Volume = {38},
Abstract = {Canopy height distributions were created from small-footprint airborne laser scanner data with an average sampling density of 1.13 points center dot m(-2) collected over 132 sample plots and 61 forest stands. Field measurements of each plot were carried out within two concentric circles corresponding to fixed areas of 200 m(2) and 300 or 400 m(2). The laser point clouds were thinned to approximately 0.25, 0.13, and 0.06 point center dot m(-2). For all comparisons, the maximum values of the first as well as last return canopy height distributions differed significantly between the full density and the thinned data. The combined effects of number of field plots, field plot sizes, and point densities on the accuracy of mean tree height, stand basal area, and stand volume predicted at stand level using a two-stage procedure combining field training data and laser data, were assessed using Monte Carlo simulation randomly selecting 75% and 50% of the field plots. The average standard deviation showed only a minor increase by decreasing point density and increased when the number of sample plots was reduced. The effects of field plot size varied with canopy structure and stem density.},
Keywords = {FOREST STANDS TREE HEIGHT CANOPY HEIGHT STEM VOLUME LIDAR METRICS AREAS},
Owner = {hanso},
Timestamp = {2011.11.17}
}

A 20-channel, dual-frequency GPS receiver collecting pseudorange and carrier phase observations was used as a stand-alone receiver to determine positional accuracy of 19 points under conifer tree canopies. The positions were determined utilizing precise satellite orbit- and clock products from the International GNSS Service. The mean positional accuracy ranged from 0.27-0.88 m for an observation period of 120 min to 0.95-3.48 m for 15 min. For 15 min observation period computed positions could not be found for 8-44% of the locations. Accuracy increased with decreasing forest stand density. Stand basal area (R2=0.11, p<0.001) and number of tree stems (R2=0.07, p<0.001) were significantly correlated with accuracy. The probability of determining a position increased with longer observation periods and decreasing number of tree stems. For natural resource applications where the costs associated with the length of the observation period on each site in field is a critical factor, differential GPS seems to be a more robust alternative than precise point positioning with GPS.

@Article{Naesset2008a,
Title = {Performance of GPS precise point positioning under conifer forest canopies},
Author = {Næsset, Erik and Gjevestad, Jon Glenn},
Journal = {Photogrammetric Engineering and Remote Sensing},
Year = {2008},
Pages = {661-668},
Volume = {74},
Abstract = {A 20-channel, dual-frequency GPS receiver collecting pseudorange and carrier phase observations was used as a stand-alone receiver to determine positional accuracy of 19 points under conifer tree canopies. The positions were determined utilizing precise satellite orbit- and clock products from the International GNSS Service. The mean positional accuracy ranged from 0.27-0.88 m for an observation period of 120 min to 0.95-3.48 m for 15 min. For 15 min observation period computed positions could not be found for 8-44% of the locations. Accuracy increased with decreasing forest stand density. Stand basal area (R2=0.11, p<0.001) and number of tree stems (R2=0.07, p<0.001) were significantly correlated with accuracy. The probability of determining a position increased with longer observation periods and decreasing number of tree stems. For natural resource applications where the costs associated with the length of the observation period on each site in field is a critical factor, differential GPS seems to be a more robust alternative than precise point positioning with GPS.},
Owner = {hanso},
Timestamp = {2011.11.17}
}

Regression models relating variables derived from airborne laser scanning (ALS) to above-ground and below-ground biomass were estimated for 1395 sample plots in young and mature coniferous forest located in ten different areas within the boreal forest zone of Norway. The sample plots were measured as part of large-scale operational forest inventories. Four different ALS instruments were used and point density varied from 0.7 to 1.2 m(-2). One variable related to canopy height and one related to canopy density were used as independent variables in the regressions. The statistical effects of area and age class were assessed by including dummy variables in the models. Tree species composition was treated as continuous variables. The proportion of explained variability was 88% for above- and 85% for below-ground biomass models. For given combinations of ALS-derived variables, the differences between the areas were up to 32% for above-ground biomass and 38% for below-ground biomass. The proportion of spruce had a significant impact on both the estimated models. The proportion of broadleaves had a significant effect on above-ground biomass only, while the effect of age class was significant only in the below-ground biomass model. Because of local effects on the biomass-ALS data relationships, it is indicated by this study that sample plots distributed over the entire area would be needed when using ALS for regional or national biomass monitoring. (C) 2008 Elsevier Inc. All rights reserved.

@Article{Naesset2008,
Title = {Estimation of above- and below-ground biomass across regions of the boreal forest zone using airborne laser},
Author = {Næsset, E. and Gobakken, T.},
Journal = {Remote Sensing of Environment},
Year = {2008},
Note = {Naesset, Erik Gobakken, Terje},
Number = {6},
Pages = {3079-3090},
Volume = {112},
Abstract = {Regression models relating variables derived from airborne laser scanning (ALS) to above-ground and below-ground biomass were estimated for 1395 sample plots in young and mature coniferous forest located in ten different areas within the boreal forest zone of Norway. The sample plots were measured as part of large-scale operational forest inventories. Four different ALS instruments were used and point density varied from 0.7 to 1.2 m(-2). One variable related to canopy height and one related to canopy density were used as independent variables in the regressions. The statistical effects of area and age class were assessed by including dummy variables in the models. Tree species composition was treated as continuous variables. The proportion of explained variability was 88% for above- and 85% for below-ground biomass models. For given combinations of ALS-derived variables, the differences between the areas were up to 32% for above-ground biomass and 38% for below-ground biomass. The proportion of spruce had a significant impact on both the estimated models. The proportion of broadleaves had a significant effect on above-ground biomass only, while the effect of age class was significant only in the below-ground biomass model. Because of local effects on the biomass-ALS data relationships, it is indicated by this study that sample plots distributed over the entire area would be needed when using ALS for regional or national biomass monitoring. (C) 2008 Elsevier Inc. All rights reserved.},
Owner = {hanso},
Timestamp = {2011.11.17}
}

A model for prediction of stand basal area and diameters at 10 percentiles of a basal area distribution was estimated from small-footprint laser scanner data from primeval conifer forest using partial least squares regression. The regression explained 44&ndash;80% and 67% of the variability of the 10 percentiles and stand basal area, respectively. The predicted percentiles, scaled by the predicted stand basal area, were used to compute diameter distributions. A cross-validation showed that the mean differences between the predicted and observed number of stems by diameter class were non-significant (<i>p</i>&gt;0.05) for 22 of 29 diameter classes. Moreover, plot volume was calculated from the predicted diameter distribution and cross-validation revealed a non-significant deviation between predicted and observed volume of -3.3% (of observed volume). An independent validation showed non-significant mean differences for 20 of 21 diameter classes for data corresponding to the model calibration data. Plot volumes calculated from the predicted diameter distributions deviated from observed volume by -4.4%. The model reproduced diameter distributions corresponding to the model calibration data (uneven-sized forest) well. However, the model is not flexible enough to reproduce normal and uniform diameter distributions. Volume estimates derived from predicted diameter distributions were generally well determined, irrespective of the observed distribution.

Imputations of missing values and optimal smoothing with massive data arrays poses a computational challenge since ordinary kriging becomes infeasible. Imputation and smoothing with standard algorithms like inverse distance weighted nearest neighbour interpolation (IDW) and interpolation on triangulated irregular networks (TIN/IP) fail to incorporate the spatial structure and ignore information beyond the neighbourhood. Multiresolution spatial models (MRSM) or approximate kriging methods adapted to handling massive data sets can be expected to do better than IDW and TIN/IP in terms of mean square errors of prediction (MSEP). We illustrate a MRSM that is efficient, computationally fast, and easy to implement. In two forestry examples with imputation of LiDAR range values the MRSM achieved a lower MSEP than IDW, TIN/IP, and fixed ranked kriging. MRSM appear as especially attractive for the construction of a DTM from last return LiDAR pulses. A third example demonstrates MRSM for efficient smoothing. Crown Copyright (C) 2007 Published by Elsevier Inc. All rights reserved.

This research reports the major evaluation results from an operational stand-based forest 3 inventory using airborne laser scanner data carried out in Norway. This is the first operational 4 inventory where data from two separate districts are combined. Laser data from two forest 5 areas of 65 and 110 km2, respectively, were used to predict six biophysical stand variables 6 used in forest planning. The predictions were based on regression equations estimated from 7 250 m2 field training plots distributed systematically throughout the two forest areas. Test 8 plots with a size of 0.1 ha were used for validation. The testing revealed standard deviations 9 between ground-truth values and predicted values of 0.58-0.85 m (3.4-5.6%) for mean and 10 dominant heights, 2.62-2.87 m2ha-1 (9.3-14.3%) for basal area, and 18.7-25.1 m3ha-1 (10.8- 11 12.8%) for stand volume. No serious bias was detected. For 10 of the 12 estimated regression 12 models there were no significant effects of district.

@Article{Naesset2007,
Title = {Airborne laser scanning as a method in operational forest inventory: status of accuracy assessments accomplished in Scandinavia},
Author = {Næsset, Erik},
Journal = {Scandinavian Journal of Forest Research},
Year = {2007},
Note = {Publikasjonen om områdetaksten i Hole og Fet (ikke hovedstudien i Hole) er nå akseptert for publisering i Scand. Journal of Forest Research},
Volume = {In press.},
Abstract = {This research reports the major evaluation results from an operational stand-based forest 3 inventory using airborne laser scanner data carried out in Norway. This is the first operational 4 inventory where data from two separate districts are combined. Laser data from two forest 5 areas of 65 and 110 km2, respectively, were used to predict six biophysical stand variables 6 used in forest planning. The predictions were based on regression equations estimated from 7 250 m2 field training plots distributed systematically throughout the two forest areas. Test 8 plots with a size of 0.1 ha were used for validation. The testing revealed standard deviations 9 between ground-truth values and predicted values of 0.58-0.85 m (3.4-5.6%) for mean and 10 dominant heights, 2.62-2.87 m2ha-1 (9.3-14.3%) for basal area, and 18.7-25.1 m3ha-1 (10.8- 11 12.8%) for stand volume. No serious bias was detected. For 10 of the 12 estimated regression 12 models there were no significant effects of district.},
Keywords = {Forest inventory laser scanning forest planning biophysical stand properties},
Owner = {hanso},
Timestamp = {2011.11.17}
}

The boreal tree line is expected to advance upwards into the mountains and northwards into the tundra due to global warming. The major objective of this study was to find out if it is possible to use high-resolution airborne laser scanner data to detect very small trees — the pioneers that are pushing the tree line up into the mountains and out onto the tundra. The study was conducted in a sub-alpine/alpine environment in southeast Norway. A total of 342 small trees of Norway spruce, Scots pine, and downy birch with tree heights ranging from 0.11 to 5.20 m were precisely georeferenced and measured in field. Laser data were collected with a pulse density of 7.7 m- 2. Three different terrain models were used to process the airborne laser point cloud in order to assess the effects of different pre-processing parameters on small tree detection. Greater than 91% of all trees > 1 m tall registered positive laser height values regardless of terrain model. For smaller trees (< 1 m), positive height values were found in 5-73% of the cases, depending on the terrain model considered. For this group of trees, the highest rate of trees with positive height values was found for spruce. The more smoothed the terrain model was, the larger the portion of the trees that had positive laser height values. The accuracy of tree height derived from the laser data indicated a systematic underestimation of true tree height by 0.40 to 1.01 m. The standard deviation for the differences between laser-derived and field-measured tree heights was 0.11-0.73 m. Commission errors, i.e., the detection of terrain objects — rocks, hummocks — as trees, increased significantly as terrain smoothing increased. Thus, if no classification of objects into classes like small trees and terrain objects is possible, many non-tree objects with a positive height value cannot be separated from those actually being trees. In a monitoring context, i.e., repeated measurements over time, we argue that most other objects like terrain structures, rocks, and hummocks will remain stable over time while the trees will change as they grow and new trees are established. Thus, this study indicates that, given a high laser pulse density and a certain density of newly established trees, it would be possible to detect a sufficient portion of newly established trees over a 10 years period to claim that tree migration is taking place.

@Article{Naesset2007a,
Title = {Using airborne laser scanning to monitor tree migration in the boreal-alpine transition zone},
Author = {Næsset, Erik and Nelson, Ross},
Journal = {Remote Sensing of Environment},
Year = {2007},
Pages = {357-369},
Volume = {110},
Abstract = {The boreal tree line is expected to advance upwards into the mountains and northwards into the tundra due to global warming. The major objective of this study was to find out if it is possible to use high-resolution airborne laser scanner data to detect very small trees -- the pioneers that are pushing the tree line up into the mountains and out onto the tundra. The study was conducted in a sub-alpine/alpine environment in southeast Norway. A total of 342 small trees of Norway spruce, Scots pine, and downy birch with tree heights ranging from 0.11 to 5.20 m were precisely georeferenced and measured in field. Laser data were collected with a pulse density of 7.7 m- 2. Three different terrain models were used to process the airborne laser point cloud in order to assess the effects of different pre-processing parameters on small tree detection. Greater than 91% of all trees > 1 m tall registered positive laser height values regardless of terrain model. For smaller trees (< 1 m), positive height values were found in 5-73% of the cases, depending on the terrain model considered. For this group of trees, the highest rate of trees with positive height values was found for spruce. The more smoothed the terrain model was, the larger the portion of the trees that had positive laser height values. The accuracy of tree height derived from the laser data indicated a systematic underestimation of true tree height by 0.40 to 1.01 m. The standard deviation for the differences between laser-derived and field-measured tree heights was 0.11-0.73 m. Commission errors, i.e., the detection of terrain objects -- rocks, hummocks -- as trees, increased significantly as terrain smoothing increased. Thus, if no classification of objects into classes like small trees and terrain objects is possible, many non-tree objects with a positive height value cannot be separated from those actually being trees. In a monitoring context, i.e., repeated measurements over time, we argue that most other objects like terrain structures, rocks, and hummocks will remain stable over time while the trees will change as they grow and new trees are established. Thus, this study indicates that, given a high laser pulse density and a certain density of newly established trees, it would be possible to detect a sufficient portion of newly established trees over a 10 years period to claim that tree migration is taking place.},
Doi = {10.1016/j.rse.2007.03.004},
Keywords = {Forest monitoring Global change Laser scanning PCQ sampling Small trees Tree growth Tree line Tree migration},
Owner = {hanso},
Timestamp = {2011.11.17},
Url = {http://www.sciencedirect.com/science/article/pii/S0034425707001198}
}

High-resolution datasets from Airborne Laser Scanning (ALS) provide information to extract the outline of single tree crowns. Laser echoes with spatial coordinates inside these single-tree crowns give the ability of measuring biophysical properties and to classify species of these single-trees. Species classification by ALS-data is based on differences in crown shape, crown density, reflectivity and distribution of foliage and branches between tree species. All of these parameters may be expressed by spatial coordinates of the point cloud or by the intensity of the backscattered signal measured by ALS. In this study we investigate mean intensity and standard deviation of intensity computed for single trees by explorative data analysis and linear discriminant analysis. We explore differences in spruce, birch, and aspen trees for different echo categories from a multiple return ALS system. We found that intensity could assist species discrimination. The overall classification accuracies obtained were from 68 to 74 %, depending on number of variables considered. In spite of the heterogeneous structure of the forest studied, the classification accuracy was fairly high. Intensity metrics computed from different echo categories influence overall accuracies by 3 to 4 %, depending on the intensity metric used in the classification. Both species reflectivity and structural characteristics within the tree crown will influence intensity recorded by ALS.

@InProceedings{Oerka2007,
Title = {Utilizing airborne laser intensity for tree species classification},
Author = {Ørka, H.O. and Næsset, E. and Bollandsås, O.M.},
Year = {2007},
Note = {Submitted},
Pages = {300-304},
Publisher = {Finnish Geodetic Institute and TKK},
Volume = {XXXVI, Part 3/W52},
Abstract = {High-resolution datasets from Airborne Laser Scanning (ALS) provide information to extract the outline of single tree crowns. Laser echoes with spatial coordinates inside these single-tree crowns give the ability of measuring biophysical properties and to classify species of these single-trees. Species classification by ALS-data is based on differences in crown shape, crown density, reflectivity and distribution of foliage and branches between tree species. All of these parameters may be expressed by spatial coordinates of the point cloud or by the intensity of the backscattered signal measured by ALS. In this study we investigate mean intensity and standard deviation of intensity computed for single trees by explorative data analysis and linear discriminant analysis. We explore differences in spruce, birch, and aspen trees for different echo categories from a multiple return ALS system. We found that intensity could assist species discrimination. The overall classification accuracies obtained were from 68 to 74 %, depending on number of variables considered. In spite of the heterogeneous structure of the forest studied, the classification accuracy was fairly high. Intensity metrics computed from different echo categories influence overall accuracies by 3 to 4 %, depending on the intensity metric used in the classification. Both species reflectivity and structural characteristics within the tree crown will influence intensity recorded by ALS.},
Keywords = {Laser scanning, High resolution, Forest, Inventory, Analysis, Classification},
Owner = {hanso},
Timestamp = {2011.11.17}
}

E. Næsset and R. Nelson, “Using scanning lidar for detection of small trees in the boreal-alpine transition zone as indicator of global change,” in Proceedings of the silvilaser 2006 conference, 2006, pp. 2-7. [Bibtex]

In a balanced experiment based on 20 field plots located in a 21 km2 Scots pine forest in southeast Norway covering age classes from newly regenerated to old forest, leaf area index (LAI) was determined in field by a LAI-2000 instrument and hemispheric photography. Based on a formalized framework, i.e., the so-called Beer-Lambert law, gap fraction derived from small-footprint airborne laser scanner data was regressed against field-measured LAI. LAI was strongly (R2 = 0.87-0.93), positively, and linearly related to the log-transformed inverse of the gap fraction derived from the laser scanner data. This was as expected according to the Beer-Lambert law, as was the absence of an intercept, producing a directly proportionality of the two variables. We estimated an extinction coefficient for the first return echoes to be 0.7, fortunately fairly stable across age classes, and this is likely to be a parameter specific for the applied laser scanner system under the given flight and system settings, such as flying altitude and laser pulse repetition frequency. It was demonstrated that airborne laser was able to detect defoliation in terms of estimated changes in LAI, by three repeated laser data acquisitions over the area where severe insect attacks were going on in between the acquisitions.

@Article{Solberg2006a,
Title = {Mapping defoliation during a severe insect attack on Scots pine using airborne laser scanning},
Author = {Solberg, Svein and Næsset, Erik and Hanssen, Kjersti Holt and Christiansen, Erik},
Journal = {Remote Sensing of Environment},
Year = {2006},
Note = {doi: DOI: 10.1016/j.rse.2006.03.001},
Number = {3-4},
Pages = {364-376},
Volume = {102},
Abstract = {In a balanced experiment based on 20 field plots located in a 21 km2 Scots pine forest in southeast Norway covering age classes from newly regenerated to old forest, leaf area index (LAI) was determined in field by a LAI-2000 instrument and hemispheric photography. Based on a formalized framework, i.e., the so-called Beer-Lambert law, gap fraction derived from small-footprint airborne laser scanner data was regressed against field-measured LAI. LAI was strongly (R2 = 0.87-0.93), positively, and linearly related to the log-transformed inverse of the gap fraction derived from the laser scanner data. This was as expected according to the Beer-Lambert law, as was the absence of an intercept, producing a directly proportionality of the two variables. We estimated an extinction coefficient for the first return echoes to be 0.7, fortunately fairly stable across age classes, and this is likely to be a parameter specific for the applied laser scanner system under the given flight and system settings, such as flying altitude and laser pulse repetition frequency. It was demonstrated that airborne laser was able to detect defoliation in terms of estimated changes in LAI, by three repeated laser data acquisitions over the area where severe insect attacks were going on in between the acquisitions.},
Doi = {10.1016/j.rse.2006.03.001},
Keywords = {Airborne laser scanning Beer-Lambert law Defoliation LAI Forest health monitoring},
Owner = {hanso},
Timestamp = {2011.11.17},
Url = {http://www.sciencedirect.com/science/article/pii/S0034425706000964}
}

The aim of this study was to assess the accuracy of basal area distributions of sample plots in coniferous forest derived from small-footprint airborne laser scanner data, and to compare the accuracy of two methods for derivation of such distributions based on: (1) two percentiles of a two-parameter Weibull and parameter recovery, and (2) a system of 10 percentiles defined across the range of observed diameters. The 12 percentiles were derived from the empirical basal area distributions of 141 plots with size 300 – 400 m(2). Regression analysis was used to relate the percentiles to various canopy height and canopy density metrics derived from the laser data. On average, the distance between transmitted laser pulses was 0.9 m on the ground. The plots were divided into three strata according to age class and site quality. The stratum-specific regressions explained 7 – 91% of the variability. Total plot volume predicted from the estimated distributions was used to assess the accuracy of the regressions. Cross-validation of the regressions revealed a bias of – 1.2 to 2.1% between predicted and ground-truth values of plot volume. The standard deviations of the differences between predicted and ground-truth values of plot volume were 15.1 – 16.4%. Neither bias nor standard deviation differed significantly between the two validated methods.

@Article{Gobakken2005,
Title = {Weibull and percentile models for lidar-based estimation of basal area distribution},
Author = {Gobakken, T. and Næsset, E.},
Journal = {Scandinavian Journal of Forest Research},
Year = {2005},
Note = {ISI Document Delivery No.: 995RN Times Cited: 0 Cited Reference Count: 45},
Number = {6},
Pages = {490-502},
Volume = {20},
Abstract = {The aim of this study was to assess the accuracy of basal area distributions of sample plots in coniferous forest derived from small-footprint airborne laser scanner data, and to compare the accuracy of two methods for derivation of such distributions based on: (1) two percentiles of a two-parameter Weibull and parameter recovery, and (2) a system of 10 percentiles defined across the range of observed diameters. The 12 percentiles were derived from the empirical basal area distributions of 141 plots with size 300 - 400 m(2). Regression analysis was used to relate the percentiles to various canopy height and canopy density metrics derived from the laser data. On average, the distance between transmitted laser pulses was 0.9 m on the ground. The plots were divided into three strata according to age class and site quality. The stratum-specific regressions explained 7 - 91% of the variability. Total plot volume predicted from the estimated distributions was used to assess the accuracy of the regressions. Cross-validation of the regressions revealed a bias of - 1.2 to 2.1% between predicted and ground-truth values of plot volume. The standard deviations of the differences between predicted and ground-truth values of plot volume were 15.1 - 16.4%. Neither bias nor standard deviation differed significantly between the two validated methods.},
Keywords = {airborne laser scanner basal area distribution diameter distribution LASER SCANNER DATA FOREST STAND CHARACTERISTICS DIAMETER DISTRIBUTION MODELS MEAN TREE HEIGHT SIZE DISTRIBUTION AIRBORNE LIDAR TIMBER VOLUME STEM VOLUME SCOTS PINE ACCURACY},
Owner = {hanso},
Timestamp = {2011.11.17}
}

Canopy height distributions were created from small-footprint airborne laser scanner data collected over 51 georeferenced field sample plots with a size of 232.9 m2 and 27 large test plots with an average size of 3435 m2. Laser data were acquired under leaf-on and leaf-off canopy conditions. The plots covered stand conditions from young forest to mature forest. The plots were divided into two categories, i.e., coniferous forest dominated by spruce and pine, and mixed forest with an average proportion of deciduous species of 31â€“42%. Height percentiles, mean and maximum height values, coefficients of variation of the heights, and canopy density at different height intervals above the ground were computed from the laser-derived canopy height distributions. In the mixed forest, corresponding metrics derived from the two laser data acquisitions were compared. In general, canopy metrics derived from the last returns were more affected by canopy conditions than the first return data. Furthermore, canopy height measures of the lower and intermediate parts of the canopy were more affected than maximum canopy height, and the variability of the height distribution tended to increase from leaf-on to leaf-off conditions. The coniferous plots were used to demonstrate to what extent canopy properties derived from airborne lasers may be affected by sensor-specific characteristics. The same laser system was used during the two acquisitions, but the repetition frequency was upgraded from 10 to 33 kHz in between the two missions. Comparison of the two acquisitions showed that the first return measurements of canopy height tended to be unaffected or shifted somewhat upwards by system upgrade and ground penetration was reduced, whereas the last return data indicated unaffected or downwards shifted canopy heights and increased penetration.

@Article{Naesset2005b,
Title = {Assessing sensor effects and effects of leaf-off and leaf-on canopy conditions on biophysical stand properties derived from small-footprint airborne laser data},
Author = {Næsset, Erik},
Journal = {Remote Sensing of Environment},
Year = {2005},
Pages = {356-370},
Volume = {98},
Abstract = {Canopy height distributions were created from small-footprint airborne laser scanner data collected over 51 georeferenced field sample plots with a size of 232.9 m2 and 27 large test plots with an average size of 3435 m2. Laser data were acquired under leaf-on and leaf-off canopy conditions. The plots covered stand conditions from young forest to mature forest. The plots were divided into two categories, i.e., coniferous forest dominated by spruce and pine, and mixed forest with an average proportion of deciduous species of 31â€“42%. Height percentiles, mean and maximum height values, coefficients of variation of the heights, and canopy density at different height intervals above the ground were computed from the laser-derived canopy height distributions. In the mixed forest, corresponding metrics derived from the two laser data acquisitions were compared. In general, canopy metrics derived from the last returns were more affected by canopy conditions than the first return data. Furthermore, canopy height measures of the lower and intermediate parts of the canopy were more affected than maximum canopy height, and the variability of the height distribution tended to increase from leaf-on to leaf-off conditions. The coniferous plots were used to demonstrate to what extent canopy properties derived from airborne lasers may be affected by sensor-specific characteristics. The same laser system was used during the two acquisitions, but the repetition frequency was upgraded from 10 to 33 kHz in between the two missions. Comparison of the two acquisitions showed that the first return measurements of canopy height tended to be unaffected or shifted somewhat upwards by system upgrade and ground penetration was reduced, whereas the last return data indicated unaffected or downwards shifted canopy heights and increased penetration.},
Keywords = {Forest inventory Canopy conditions Canopy density Canopy height Laser scanning},
Owner = {hanso},
Timestamp = {2011.11.17}
}

Mean tree height, dominant height, mean diameter, stem number, basal area, and timber volume of 233 field sample plots were estimated from various canopy height and canopy density metrics- derived by means of a small-footprint laser scanner over young and mature forest stands- using ordinary least-squares (OLS) regression analysis, seemingly unrelated regression (SUR), and partial least-squares (PLS) regression. The sample plots were distributed systematically throughout two separate inventory areas with size 1000 and 6500 ha, respectively. The plots were divided into three predefined strata. Separate regression models were estimated for each inventory as well as common models utilizing the plots of both inventories simultaneously. In the models estimated by combining data from the two areas, the statistical effect of inventory was found to be significant (p<0.05) in the mean height models only. A total of 115 test stands and plots with size 0.3-11.7 ha were used to validate the estimated regression models. The bias and standard deviations (parenthesized) of the differences between predicted and ground reference values of mean height, dominant height, mean diameter, stem number, basal area, and volume were -5.5% to 4.7% (3.1-7.3%), -6.0% to 0.4% (2.9-8.2%), -0.2% to 7.9% (5.5-15.8%), -21.3% to 12.5% (13.4-29.3%), -7.3% to 8.4% (7.1-13.6%), and -3.9% to 10.1% (8.3-14.9%), respectively. It was revealed that only minor discrepancies occurred between the three investigated estimation techniques. None of the techniques provided predicted values that were superior to the other techniques over all combinations of strata and variables. (C) 2004 Elsevier Inc. All rights reserved.

@Article{Naesset2005d,
Title = {Comparing regression methods in estimation of biophysical properties of forest stands from two different inventories using laser scanner data},
Author = {Næsset, E. and Bollandsås, O. M. and Gobakken, T.},
Journal = {Remote Sensing of Environment},
Year = {2005},
Note = {ISI Document Delivery No.: 897ZZ Times Cited: 3 Cited Reference Count: 42},
Number = {4},
Pages = {541-553},
Volume = {94},
Abstract = {Mean tree height, dominant height, mean diameter, stem number, basal area, and timber volume of 233 field sample plots were estimated from various canopy height and canopy density metrics- derived by means of a small-footprint laser scanner over young and mature forest stands- using ordinary least-squares (OLS) regression analysis, seemingly unrelated regression (SUR), and partial least-squares (PLS) regression. The sample plots were distributed systematically throughout two separate inventory areas with size 1000 and 6500 ha, respectively. The plots were divided into three predefined strata. Separate regression models were estimated for each inventory as well as common models utilizing the plots of both inventories simultaneously. In the models estimated by combining data from the two areas, the statistical effect of inventory was found to be significant (p<0.05) in the mean height models only. A total of 115 test stands and plots with size 0.3-11.7 ha were used to validate the estimated regression models. The bias and standard deviations (parenthesized) of the differences between predicted and ground reference values of mean height, dominant height, mean diameter, stem number, basal area, and volume were -5.5% to 4.7% (3.1-7.3%), -6.0% to 0.4% (2.9-8.2%), -0.2% to 7.9% (5.5-15.8%), -21.3% to 12.5% (13.4-29.3%), -7.3% to 8.4% (7.1-13.6%), and -3.9% to 10.1% (8.3-14.9%), respectively. It was revealed that only minor discrepancies occurred between the three investigated estimation techniques. None of the techniques provided predicted values that were superior to the other techniques over all combinations of strata and variables. (C) 2004 Elsevier Inc. All rights reserved.},
Keywords = {forest inventory laser scanning regression techniques LEAST-SQUARES REGRESSION TREE HEIGHT BASAL AREA AIRBORNE LIDAR STEM VOLUME CANOPY BIOMASS ACCURACY MODELS},
Owner = {hanso},
Timestamp = {2011.11.17}
}

Canopy height distributions were created from small-footprint airborne laser scanner data with a sampling density of 0.9-1.2 m(-2) collected over 133 georeferenced field sample plots and 56 forest stands located in young and mature forest. The plot size was 300-400 m 2 and the average stand size was 1.7 ha. Spruce and pine were the dominant tree species. Canopy height distributions were created from both first and last pulse data. The laser data were acquired in 1999 and 2001. Height percentiles, mean and maximum height values, coefficients of variation of the heights, and canopy density at different height intervals above the ground were computed from the laser-derived canopy height distributions. Corresponding metrics derived from the 1999 and 2001 laser datasets were compared. Forty-five of 54 metrics derived from the first pulse data changed their values significantly due to forest growth. The upper height percentiles increased their values more than the field-based height growth estimates. The 50 and 90 height percentiles increased by 0.4-1.3 in whereas the field-estimated mean height increased by 0.2-0.9 m. Metrics derived from the last pulse data were less influenced by growth. Mean tree height (h(L)), basal area (G), and volume (V) were regressed against the laser-derived variables to predict corresponding values of hL, G, and V based on the 1999 and 2001 laser data, respectively. Forest growth was estimated as the difference between the 2001 and 1999 estimates. Laser data were able to predict a significant growth in all the three biophysical variables over the 2-year period. However, the accuracy of the predictions was poor. In most cases the predictions were biased and the precision was low. Finally, several key issues of particular relevance to laser-based monitoring of forest growth are discussed. (c) 2005 Elsevier Inc. All rights reserved.

@Article{Naesset2005a,
Title = {Estimating forest growth using canopy metrics derived from airborne laser scanner data},
Author = {Næsset, E. and Gobakken, T.},
Journal = {Remote Sensing of Environment},
Year = {2005},
Note = {ISI Document Delivery No.: 948NG Times Cited: 2 Cited Reference Count: 36},
Number = {3-4},
Pages = {453-465},
Volume = {96},
Abstract = {Canopy height distributions were created from small-footprint airborne laser scanner data with a sampling density of 0.9-1.2 m(-2) collected over 133 georeferenced field sample plots and 56 forest stands located in young and mature forest. The plot size was 300-400 m 2 and the average stand size was 1.7 ha. Spruce and pine were the dominant tree species. Canopy height distributions were created from both first and last pulse data. The laser data were acquired in 1999 and 2001. Height percentiles, mean and maximum height values, coefficients of variation of the heights, and canopy density at different height intervals above the ground were computed from the laser-derived canopy height distributions. Corresponding metrics derived from the 1999 and 2001 laser datasets were compared. Forty-five of 54 metrics derived from the first pulse data changed their values significantly due to forest growth. The upper height percentiles increased their values more than the field-based height growth estimates. The 50 and 90 height percentiles increased by 0.4-1.3 in whereas the field-estimated mean height increased by 0.2-0.9 m. Metrics derived from the last pulse data were less influenced by growth. Mean tree height (h(L)), basal area (G), and volume (V) were regressed against the laser-derived variables to predict corresponding values of hL, G, and V based on the 1999 and 2001 laser data, respectively. Forest growth was estimated as the difference between the 2001 and 1999 estimates. Laser data were able to predict a significant growth in all the three biophysical variables over the 2-year period. However, the accuracy of the predictions was poor. In most cases the predictions were biased and the precision was low. Finally, several key issues of particular relevance to laser-based monitoring of forest growth are discussed. (c) 2005 Elsevier Inc. All rights reserved.},
Keywords = {forest growth forest monitoring laser scanning canopy height canopy density MEAN TREE HEIGHT STAND CHARACTERISTICS VOLUME LIDAR INVENTORY PREDICTION ACCURACY MODELS},
Owner = {hanso},
Timestamp = {2011.11.17}
}

Intergovernmental Panel on Climate Change under the UN finalised in 2004 the report “Good Practice Guidance for Estimating and Reporting of Emissions and Removals from Land Use, Landuse Change and Forestry”. The present report describes the data material and the methods used to provide such estimates for Norway for the period from 1990. Land-use changes cause changes in carbon storage, thus indirectly emissions and removals of CO2. Removals of CO2 in Norway due to land-use change are relatively insignificant compared to sequestration in existing forest. For 2003, the net sequestration of CO2 from this sector has been estimated at 21 million tonnes. That would correspond to about 38% of the total anthropogenic greenhouse gas emissions. The net sequestration increased by approximately 60 per cent from 1990 to 2003.

@TechReport{Rypdal2005,
Title = {Emissions and removals of greenhouse gases from land use, land-use change and forestry in Norway},
Author = {Rypdal, Kristin and Bloch, V.V.H. and Flugsrud, Kjetil and Gobakken, T. and Hoem, B. and Tomter, Stein M. and Aalde, Harald},
Year = {2005},
Number = {11/05},
Abstract = {Intergovernmental Panel on Climate Change under the UN finalised in 2004 the report “Good Practice Guidance for Estimating and Reporting of Emissions and Removals from Land Use, Landuse Change and Forestry”. The present report describes the data material and the methods used to provide such estimates for Norway for the period from 1990. Land-use changes cause changes in carbon storage, thus indirectly emissions and removals of CO2. Removals of CO2 in Norway due to land-use change are relatively insignificant compared to sequestration in existing forest. For 2003, the net sequestration of CO2 from this sector has been estimated at 21 million tonnes. That would correspond to about 38% of the total anthropogenic greenhouse gas emissions. The net sequestration increased by approximately 60 per cent from 1990 to 2003.},
Keywords = {Arealbruk arealinngrep klimagasser avskoging skogreisning biomasse Land use land-use change greenhouse gases deforestation afforestation biomass}
}

Evaluations of inventory methods usually end when precision and bias are quantified. Additional information on the appropriateness of a method may be provided through cost-plus-loss analyses, where the total costs are calculated as the sum of net present value (NPV) losses, i.e. expected economic losses as a result of future incorrect decisions due to errors in measurements, and inventory costs. The aim of the study was to compare inventories of basal area, dominant height and number of trees per hectare based on photo-interpretation and laser scanning from two sites in Norway by means of cost-plus-loss analyses. In general, more precise estimates were found for laser scanning than for photo-interpretation, while the biases were about equally distributed between the two methods. On average for the two sites, the inventory costs, NPV losses and total costs for photo-interpretation were about 6, 49 and 54 euros ha(-1), respectively, while they were 11, 13 and 25 euros ha(-1) for laser scanning. The data used for the comparison were limited to two sites and 77 stands, and certain simplifying assumptions were made in the cost-plus-loss analyses. Still, there is reason to believe that the results of the study are of general validity with respect to the main conclusion when comparing the two methods.

@Article{Eid2004,
Title = {Comparing stand inventories for large areas based on photo-interpretation and laser scanning by means of cost-plus-loss analyses},
Author = {Eid, T. and Gobakken, T. and Næsset, E.},
Journal = {Scandinavian Journal of Forest Research},
Year = {2004},
Note = {ISI Document Delivery No.: 883YA Times Cited: 6 Cited Reference Count: 33},
Number = {6},
Pages = {512-523},
Volume = {19},
Abstract = {Evaluations of inventory methods usually end when precision and bias are quantified. Additional information on the appropriateness of a method may be provided through cost-plus-loss analyses, where the total costs are calculated as the sum of net present value (NPV) losses, i.e. expected economic losses as a result of future incorrect decisions due to errors in measurements, and inventory costs. The aim of the study was to compare inventories of basal area, dominant height and number of trees per hectare based on photo-interpretation and laser scanning from two sites in Norway by means of cost-plus-loss analyses. In general, more precise estimates were found for laser scanning than for photo-interpretation, while the biases were about equally distributed between the two methods. On average for the two sites, the inventory costs, NPV losses and total costs for photo-interpretation were about 6, 49 and 54 euros ha(-1), respectively, while they were 11, 13 and 25 euros ha(-1) for laser scanning. The data used for the comparison were limited to two sites and 77 stands, and certain simplifying assumptions were made in the cost-plus-loss analyses. Still, there is reason to believe that the results of the study are of general validity with respect to the main conclusion when comparing the two methods.},
Keywords = {bias decision making economic losses precision FOREST STANDS TREE HEIGHT NUMBER MODELS VOLUME STEMS},
Owner = {hanso},
Timestamp = {2011.11.17}
}

Diameter and basal area distributions are used in many forest management planning packages for predicting stand Volume and growth. The distribution parameters and the 24 and 93 percentiles for parameter recovery of a two-parameter Weibull were derived for empirical diameter and basal area distributions of 54 plots of 3740 m 2 each. Regression analysis was used to relate the distribution parameters and percentiles to various canopy height and canopy density metrics derived from airborne laser scanner data over young and mature coniferous forest. On average, the distance between transmitted laser pulses was 1.0 in on the ground. Aerial photo-interpretation was used to divide the plots into three strata according to age class and site quality. The stratum-specific regressions explained 20-93% of the variability in the observed percentiles. Total plot volume predicted from the estimated distributions was used to assess the accuracy of the regressions. Cross-validation of the regressions revealed a bias of -4.8 to 2.7% between predicted and ground-truth values of plot volume when the predicted frequencies of the diameter and basal area distributions were scaled to ground-truth stem number (N) and basal area (G), respectively The standard deviations (SD) of the differences between predicted and ground-truth values of plot volume were 5.6-29.1%. However, when the scaling variables (N and G) were predicted from the laser data, the bias of plot volume determined by cross-validation was -4.7 to 6.6% and the SD was 11.4-24.2%.

@Article{Gobakken2004,
Title = {Estimation of diameter and basal area distributions in coniferous forest by means of airborne laser scanner data},
Author = {Gobakken, T. and Næsset, E.},
Journal = {Scandinavian Journal of Forest Research},
Year = {2004},
Note = {ISI Document Delivery No.: 883YA Times Cited: 4 Cited Reference Count: 57},
Number = {6},
Pages = {529-542},
Volume = {19},
Abstract = {Diameter and basal area distributions are used in many forest management planning packages for predicting stand Volume and growth. The distribution parameters and the 24 and 93 percentiles for parameter recovery of a two-parameter Weibull were derived for empirical diameter and basal area distributions of 54 plots of 3740 m 2 each. Regression analysis was used to relate the distribution parameters and percentiles to various canopy height and canopy density metrics derived from airborne laser scanner data over young and mature coniferous forest. On average, the distance between transmitted laser pulses was 1.0 in on the ground. Aerial photo-interpretation was used to divide the plots into three strata according to age class and site quality. The stratum-specific regressions explained 20-93% of the variability in the observed percentiles. Total plot volume predicted from the estimated distributions was used to assess the accuracy of the regressions. Cross-validation of the regressions revealed a bias of -4.8 to 2.7% between predicted and ground-truth values of plot volume when the predicted frequencies of the diameter and basal area distributions were scaled to ground-truth stem number (N) and basal area (G), respectively The standard deviations (SD) of the differences between predicted and ground-truth values of plot volume were 5.6-29.1%. However, when the scaling variables (N and G) were predicted from the laser data, the bias of plot volume determined by cross-validation was -4.7 to 6.6% and the SD was 11.4-24.2%.},
Keywords = {airborne laser scanning basal area distribution diameter distribution Weibull distribution STAND CHARACTERISTICS TREE HEIGHT BIOPHYSICAL PROPERTIES WEIBULL DISTRIBUTION LIDAR VOLUME CANOPY BIOMASS MODEL PREDICTION},
Owner = {hanso},
Timestamp = {2011.11.17}
}

Regression models for above-ground and below-ground biomass were estimated for 143 sample plots in young and mature coniferous forest. Various canopy height and canopy density metrics derived from the canopy height distributions of first as well as last pulse laser scanner data with a sampling density of approximately 1.1 m-2 were used as independent variables in the regressions. Each of the selected models comprised at least one variable related to canopy height and one related to canopy density. The models for above-ground biomass explained 92% of the variability whereas the models for below-ground biomass explained 86%. The analysis indicated that forest type did not have any significant impact on the estimated models.

@InProceedings{Naesset2004,
Title = {Estimation of Above- and Below-Ground Biomass in Boreal Forest Ecosystems},
Author = {Næsset, Erik},
Year = {2004},
Month = {October, 3-6 2004},
Pages = {145-148},
Publisher = {International society of photogrammetry and remote sensing. International archies of photogrammetry, remote sensing and spatial information sciences.},
Volume = {XXXVI, PART 8/W2},
Abstract = {Regression models for above-ground and below-ground biomass were estimated for 143 sample plots in young and mature coniferous forest. Various canopy height and canopy density metrics derived from the canopy height distributions of first as well as last pulse laser scanner data with a sampling density of approximately 1.1 m-2 were used as independent variables in the regressions. Each of the selected models comprised at least one variable related to canopy height and one related to canopy density. The models for above-ground biomass explained 92% of the variability whereas the models for below-ground biomass explained 86%. The analysis indicated that forest type did not have any significant impact on the estimated models.},
Owner = {hanso},
Timestamp = {2011.11.17}
}

Mean tree height, dominant height, mean diameter, stem number, basal area and timber volume of 116 georeferenced field sample plots were estimated from various canopy height and canopy density metrics derived by means of a small-footprint laser scanner over young and mature forest stands using regression analysis. The sample plots were distributed systematically throughout a 6500 ha study area, and the size of each plot was 232.9 m2. Regressions for coniferous forest explained 60-97% of the variability in ground reference values of the six studied characteristics. A proposed practical two-phase procedure for prediction of corresponding characteristics of entire forest stands was tested. Fifty-seven test plots within the study area with a size of approximately 3740 m2 each were divided into 232.9 m2 regular grid cells. The six examined characteristics were predicted for each grid cell from the corresponding laser data using the estimated regression equations. Average values for each test plot were computed and compared with ground-based estimates measured over the entire plot. The bias and standard deviations of the differences between predicted and ground reference values (in parentheses) of mean height, dominant height, mean diameter, stem number, basal area and volume were -0.58 to -0.85 m (0.64-1.01 m), -0.60 to -0.99 m (0.67-0.84 m), 0.15-0.74 cm (1.33-2.42 cm), 34-108 ha-1 (97-466 ha-1), 0.43-2.51 m2 ha-1 (1.83-3.94 m2 ha-1) and 5.9-16.1 m3 ha-1 (15.1-35.1 m3 ha-1), respectively.

Canopy height distributions were created from small-footprint airborne laser scanner data collected over 133 georeferenced field sample plots and 56 forest stands located in young and mature forest. The plot size was 300-400 m2 and the average stand size was 1.7 ha. Spruce and pine were the dominant tree species. Canopy height distributions were created from both first and last pulse data. The laser data were acquired from two different flying altitudes, i.e., 530-540 m and 840-850 m above ground. Height percentiles, mean and maximum height values, coefficients of variation of the heights, and canopy density at different height intervals above the ground were computed from the laser-derived canopy height distributions. Corresponding metrics derived from the two different flying altitudes were compared. Only one of 54 metrics derived from the first pulse data differed significantly between flying altitudes. For the last pulse data, the mean values of the height percentiles were up to 50 cm higher than the corresponding values of the low altitude data. The high altitude data yielded significantly higher values for most of the canopy density measures. The standard deviation for the differences between high and low flying altitude for each of the metrics was estimated. The standard deviations for the height percentiles ranged from 0.07 cm to 0.30 cm in the forest stands, indicating a large degree of stability between repeated flight over-passes. The effect of variable flying altitude on mean tree height (hL), stand basal area (G), and stand volume (V) estimated from the laser-derived height and density measures using a two-stage inventory procedure, was assessed by randomly combining laser data from the two flying altitudes for each individual sample plot and forest stand. The sample plots were used as training data to calibrate the models. The random assignment was repeated 10,000 times. The results of the 10,000 trials indicated that the precision of the estimated values of hL, G, and V was robust against alterations in flying altitude.

@Article{Naesset2004c,
Title = {Effects of different flying altitudes on biophysical stand properties estimated from canopy height and density measured with a small-footprint airborne scanning laser},
Author = {Næsset, Erik},
Journal = {Remote Sensing of Environment},
Year = {2004},
Pages = {243â€“255},
Volume = {91},
Abstract = {Canopy height distributions were created from small-footprint airborne laser scanner data collected over 133 georeferenced field sample plots and 56 forest stands located in young and mature forest. The plot size was 300-400 m2 and the average stand size was 1.7 ha. Spruce and pine were the dominant tree species. Canopy height distributions were created from both first and last pulse data. The laser data were acquired from two different flying altitudes, i.e., 530-540 m and 840-850 m above ground. Height percentiles, mean and maximum height values, coefficients of variation of the heights, and canopy density at different height intervals above the ground were computed from the laser-derived canopy height distributions. Corresponding metrics derived from the two different flying altitudes were compared. Only one of 54 metrics derived from the first pulse data differed significantly between flying altitudes. For the last pulse data, the mean values of the height percentiles were up to 50 cm higher than the corresponding values of the low altitude data. The high altitude data yielded significantly higher values for most of the canopy density measures. The standard deviation for the differences between high and low flying altitude for each of the metrics was estimated. The standard deviations for the height percentiles ranged from 0.07 cm to 0.30 cm in the forest stands, indicating a large degree of stability between repeated flight over-passes. The effect of variable flying altitude on mean tree height (hL), stand basal area (G), and stand volume (V) estimated from the laser-derived height and density measures using a two-stage inventory procedure, was assessed by randomly combining laser data from the two flying altitudes for each individual sample plot and forest stand. The sample plots were used as training data to calibrate the models. The random assignment was repeated 10,000 times. The results of the 10,000 trials indicated that the precision of the estimated values of hL, G, and V was robust against alterations in flying altitude.},
Keywords = {Forest inventory Laser scanning Canopy height Canopy density Monte Carlo simulation},
Owner = {hanso},
Timestamp = {2011.11.17}
}

This article reviews the research and application of airborne laser scanning for forest inventory in Finland, Norway and Sweden. The first experiments with scanning lasers for forest inventory were conducted in 1991 using the FLASH system, a full-waveform experimental laser developed by the Swedish Defence Research Institute. In Finland at the same time, the HUTSCAT profiling radar provided experiences that inspired the following laser scanning research. Since 1995, data from commercially operated time-of-flight scanning lasers (e.g. TopEye, Optech ALTM and TopoSys) have been used. Especially in Norway, the main objective has been to develop methods that are directly suited for practical forest inventory at the stand level. Mean tree height, stand volume and basal area have been the most important forest mensurational parameters of interest. Laser data have been related to field training plot measurements using regression techniques, and these relationships have been used to predict corresponding properties in all forest stands in an area. Experiences from Finland, Norway and Sweden show that retrieval of stem volume and mean tree height on a stand level from laser scanner data performs as well as, or better than, photogrammetric methods, and better than other remote sensing methods. Laser scanning is, therefore, now beginning to be used operationally in large-area forest inventories. In Finland and Sweden, research has also been done into the identification of single trees and estimation of single-tree properties, such as tree position, tree height, crown width, stem diameter and tree species. In coniferous stands, up to 90% of the trees represented by stem volume have been correctly identified from canopy height models, and the tree height has been estimated with a root mean square error of around 0.6 m. It is significantly more difficult to identify suppressed trees than dominant trees. Spruce and pine have been discriminated on a single-tree level with 95% accuracy. The application of densely sampled laser scanner data to change detection, such as growth and cutting, has also been demonstrated.

@Article{Naesset2004d,
Title = {Laser scanning of forest resources: The Nordic experience},
Author = {Næsset, E. and Gobakken, T. and Holmgren, J. and Hyyppä, H. and Hyyppä, J. and Maltamo, M. and Nilsson, M. and Olsson, H. and Persson, A. and SÃ¶derman, U.},
Journal = {Scandinavian Journal of Forest Research},
Year = {2004},
Note = {ISI Document Delivery No.: 883YA Times Cited: 11 Cited Reference Count: 57},
Number = {6},
Pages = {482-499},
Volume = {19},
Abstract = {This article reviews the research and application of airborne laser scanning for forest inventory in Finland, Norway and Sweden. The first experiments with scanning lasers for forest inventory were conducted in 1991 using the FLASH system, a full-waveform experimental laser developed by the Swedish Defence Research Institute. In Finland at the same time, the HUTSCAT profiling radar provided experiences that inspired the following laser scanning research. Since 1995, data from commercially operated time-of-flight scanning lasers (e.g. TopEye, Optech ALTM and TopoSys) have been used. Especially in Norway, the main objective has been to develop methods that are directly suited for practical forest inventory at the stand level. Mean tree height, stand volume and basal area have been the most important forest mensurational parameters of interest. Laser data have been related to field training plot measurements using regression techniques, and these relationships have been used to predict corresponding properties in all forest stands in an area. Experiences from Finland, Norway and Sweden show that retrieval of stem volume and mean tree height on a stand level from laser scanner data performs as well as, or better than, photogrammetric methods, and better than other remote sensing methods. Laser scanning is, therefore, now beginning to be used operationally in large-area forest inventories. In Finland and Sweden, research has also been done into the identification of single trees and estimation of single-tree properties, such as tree position, tree height, crown width, stem diameter and tree species. In coniferous stands, up to 90% of the trees represented by stem volume have been correctly identified from canopy height models, and the tree height has been estimated with a root mean square error of around 0.6 m. It is significantly more difficult to identify suppressed trees than dominant trees. Spruce and pine have been discriminated on a single-tree level with 95% accuracy. The application of densely sampled laser scanner data to change detection, such as growth and cutting, has also been demonstrated.},
Keywords = {airborne laser scanning canopy height forest inventory timber volume tree segmentation BOREAL NATURE-RESERVE DEPTH-SOUNDING LIDAR MEAN TREE HEIGHT POINT ACCURACY SMALL-FOOTPRINT GLONASS OBSERVATIONS DIFFERENTIAL GPS CANOPY HEIGHT TIMBER VOLUME STEM VOLUME},
Owner = {hanso},
Timestamp = {2011.11.17}
}

A stand-based model for predicting basal-area mean diameter growth for Norway spruce (Picea abies (L.) Karst.) in young mixed stands of spruce and birch (Betula pendula Roth, B. pubescens Ehrh.) was developed and compared with two existing growth models developed for older stands. The main data were from experiments with four different pre-commercial thinning regimes. A multiplicative model with four independent variables was found suitable. The independent variables were total number of trees per hectare of all the species, site index, dominant height of spruce, and a measure of competition between birch and spruce, i.e. dominant height of spruce divided by the dominant height of birch multiplied by the proportion of spruce of total number of trees. The R-2 value was 0.59 and the coefficient of variation was 12%. A test with an independent data set from the National Forest Inventory (NFI) indicated that the function developed in this study is suitable for young stands at medium to highly productive areas. Large deviations between observed and predicted growth for the two existing functions were revealed in highly productive stands. The tests based on data from the NFI also indicated that the existing function developed for spruce in older mixed stands is suitable for practical purposes for young stands. (C) 2002 Elsevier Science B.V. All rights reserved.

@Article{Gobakken2002,
Title = {Spruce diameter growth in young mixed stands of Norway spruce (Picea abies (L.) Karst.) and birch (Betula pendula Roth B-pubescens Ehrh.)},
Author = {Gobakken, T. and Næsset, E.},
Journal = {Forest Ecology and Management},
Year = {2002},
Note = {ISI Document Delivery No.: 608PB Times Cited: 0 Cited Reference Count: 35},
Number = {3},
Pages = {297-308},
Volume = {171},
Abstract = {A stand-based model for predicting basal-area mean diameter growth for Norway spruce (Picea abies (L.) Karst.) in young mixed stands of spruce and birch (Betula pendula Roth, B. pubescens Ehrh.) was developed and compared with two existing growth models developed for older stands. The main data were from experiments with four different pre-commercial thinning regimes. A multiplicative model with four independent variables was found suitable. The independent variables were total number of trees per hectare of all the species, site index, dominant height of spruce, and a measure of competition between birch and spruce, i.e. dominant height of spruce divided by the dominant height of birch multiplied by the proportion of spruce of total number of trees. The R-2 value was 0.59 and the coefficient of variation was 12%. A test with an independent data set from the National Forest Inventory (NFI) indicated that the function developed in this study is suitable for young stands at medium to highly productive areas. Large deviations between observed and predicted growth for the two existing functions were revealed in highly productive stands. The tests based on data from the NFI also indicated that the existing function developed for spruce in older mixed stands is suitable for practical purposes for young stands. (C) 2002 Elsevier Science B.V. All rights reserved.},
Keywords = {Betula pubescens Betula pendula growth model mixed stand Picea abies},
Owner = {hanso},
Timestamp = {2011.11.17}
}

The mean tree height, dominant height, mean diameter, stem number, basal area, and timber volume of 144 georeferenced field sample plots were estimated from various canopy height and canopy density metrics derived by means of a small-footprint laser scanner over young and mature forest stands using regression analysis. The sample plots were distributed systematically throughout a 1000-ha study area, and the size of each plot was 200 m2. On the average, the distance between transmitted laser pulses was 0.9 m on the ground. The plots were divided into three strata according to age class and site quality. The stratum-specific regressions explained 82Â¯95%, 74Â¯93%, 39Â¯78%, 50Â¯68%, 69Â¯89%, and 80Â¯93% of the variability in ground-truth mean height, dominant height, mean diameter, stem number, basal area, and volume, respectively. A proposed practical two-stage procedure for prediction of corresponding characteristics of entire forest stands was tested. Sixty-one stands within the study area, with an average size of 1.6 ha each, were divided into 200 m2 regular grid cells. The six examined characteristics were predicted for each grid cell from the corresponding laser data utilizing the estimated regression equations. Average values for each stand was computed. Most stand level predictions were unbiased (P>.05). Standard deviations of the differences between predicted and ground-truth values of mean height, dominant height, mean diameter, stem number, basal area, and volume were 0.61Â¯1.17 m, 0.70Â¯1.33 m, 1.37Â¯1.61 cm, 16.9Â¯22.2% (128Â¯400 ha-1), 8.6Â¯11.7% (2.33Â¯2.54 m2 ha-1), and 11.4Â¯14.2% (18.3Â¯31.9 m3 ha-1), respectively.

@Article{Naesset2002b,
Title = {Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data},
Author = {Næsset, Erik},
Journal = {Remote Sensing of Environment},
Year = {2002},
Pages = {88-99},
Volume = {80},
Abstract = {The mean tree height, dominant height, mean diameter, stem number, basal area, and timber volume of 144 georeferenced field sample plots were estimated from various canopy height and canopy density metrics derived by means of a small-footprint laser scanner over young and mature forest stands using regression analysis. The sample plots were distributed systematically throughout a 1000-ha study area, and the size of each plot was 200 m2. On the average, the distance between transmitted laser pulses was 0.9 m on the ground. The plots were divided into three strata according to age class and site quality. The stratum-specific regressions explained 82Â¯95%, 74Â¯93%, 39Â¯78%, 50Â¯68%, 69Â¯89%, and 80Â¯93% of the variability in ground-truth mean height, dominant height, mean diameter, stem number, basal area, and volume, respectively. A proposed practical two-stage procedure for prediction of corresponding characteristics of entire forest stands was tested. Sixty-one stands within the study area, with an average size of 1.6 ha each, were divided into 200 m2 regular grid cells. The six examined characteristics were predicted for each grid cell from the corresponding laser data utilizing the estimated regression equations. Average values for each stand was computed. Most stand level predictions were unbiased (P>.05). Standard deviations of the differences between predicted and ground-truth values of mean height, dominant height, mean diameter, stem number, basal area, and volume were 0.61Â¯1.17 m, 0.70Â¯1.33 m, 1.37Â¯1.61 cm, 16.9Â¯22.2% (128Â¯400 ha-1), 8.6Â¯11.7% (2.33Â¯2.54 m2 ha-1), and 11.4Â¯14.2% (18.3Â¯31.9 m3 ha-1), respectively.},
Keywords = {laser scanning},
Owner = {hanso},
Timestamp = {2011.11.17}
}

The mean tree height of 73 forest stands in a 1000 ha forest area was determined from canopy heights generated by automatic image matching using a digital photogrammetric workstation and digitized panchromatic aerial photographs with a scale of 1:15000. First, the mean height of each stand was computed as the arithmetic mean of the quantile corresponding to the 75 percentile of the distribution of the canopy heights from the image matching within square grid cells with cell sizes of 236-400 m2. The mean heights from the image matching underestimated the true heights by 5.42 m. The age class of the forest and the parameters that determine the behaviour of the image matching algorithm significantly affected the bias. Second, field-measured mean tree heights of 165 georeferenced sample plots distributed systematically throughout the 1000 ha forest area were regressed against the mean heights derived from the image matching. The regression equations were used to predict the mean heights of the 73 stands. In very young forest stands, the predicted mean heights overestimated the true heights by 0.4 m, and the standard deviation for the differences between predicted heights and ground-truth was 0.9-1.0 m. In young and mature stands, the average difference between predicted height and ground-truth ranged between -1.6 m and 0.5 m, and the standard deviation ranged from 1.1 m to 2.1 m.

@Article{Naesset2002c,
Title = {Determination of mean tree height of forest stands by means of digital photogrammetry.},
Author = {Næsset, Erik},
Journal = {Scandinavian Journal of Forest Research},
Year = {2002},
Pages = {446-459},
Volume = {17},
Abstract = {The mean tree height of 73 forest stands in a 1000 ha forest area was determined from canopy heights generated by automatic image matching using a digital photogrammetric workstation and digitized panchromatic aerial photographs with a scale of 1:15000. First, the mean height of each stand was computed as the arithmetic mean of the quantile corresponding to the 75 percentile of the distribution of the canopy heights from the image matching within square grid cells with cell sizes of 236-400 m2. The mean heights from the image matching underestimated the true heights by 5.42 m. The age class of the forest and the parameters that determine the behaviour of the image matching algorithm significantly affected the bias. Second, field-measured mean tree heights of 165 georeferenced sample plots distributed systematically throughout the 1000 ha forest area were regressed against the mean heights derived from the image matching. The regression equations were used to predict the mean heights of the 73 stands. In very young forest stands, the predicted mean heights overestimated the true heights by 0.4 m, and the standard deviation for the differences between predicted heights and ground-truth was 0.9-1.0 m. In young and mature stands, the average difference between predicted height and ground-truth ranged between -1.6 m and 0.5 m, and the standard deviation ranged from 1.1 m to 2.1 m.},
Keywords = {forest inventory digital photogrammetry tree heights},
Owner = {hanso},
Timestamp = {2011.11.17}
}

E. Næsset and T. Jonmeister, “Assessing point accuracy of dgps under forest canopy before data acquisition, in the field and after postprocessing,” Scandinavian journal of forest research, vol. 17, iss. 4, pp. 351-358, 2002. [Bibtex][Abstract]

A low-cost, hand-held, 10-channel, single-frequency Global Positioning System (GPS) receiver observing pseudorange and carrier phase was used to determine the positional accuracy of 35 points under tree canopies. The mean position error based on differential postprocessing ranged from 0.49 to 3.60 m for 2-20 min of observation at points with basal area <30 m2 ha-1. For points with basal area S45 m2 ha-1 the mean position error ranged from 2.15 to 5.60 m. Regression analysis revealed that basal area and observation period were the most significant factors to predict the position error that could be obtained by GPS data collection before or during data acquisition (R2 = 0.37). After differential postprocessing, the most significant factors to predict position error were the standard deviation reported by the postprocessing software and whether both pseudorange and carrier phase were used to compute the coordinates or pseudorange only (R2 = 0.57). The position error decreased with decreasing density of forest, increasing length of observation period, decreasing standard deviation, and combined use of pseudorange and carrier phase.

@Article{Naesset2002e,
Title = {Assessing point accuracy of DGPS under forest canopy before data acquisition, in the field and after postprocessing},
Author = {Næsset, Erik and Jonmeister, Tobias},
Journal = {Scandinavian Journal of Forest Research},
Year = {2002},
Number = {4},
Pages = {351 - 358},
Volume = {17},
Abstract = {A low-cost, hand-held, 10-channel, single-frequency Global Positioning System (GPS) receiver observing pseudorange and carrier phase was used to determine the positional accuracy of 35 points under tree canopies. The mean position error based on differential postprocessing ranged from 0.49 to 3.60 m for 2-20 min of observation at points with basal area <30 m2 ha-1. For points with basal area S45 m2 ha-1 the mean position error ranged from 2.15 to 5.60 m. Regression analysis revealed that basal area and observation period were the most significant factors to predict the position error that could be obtained by GPS data collection before or during data acquisition (R2 = 0.37). After differential postprocessing, the most significant factors to predict position error were the standard deviation reported by the postprocessing software and whether both pseudorange and carrier phase were used to compute the coordinates or pseudorange only (R2 = 0.57). The position error decreased with decreasing density of forest, increasing length of observation period, decreasing standard deviation, and combined use of pseudorange and carrier phase.},
Owner = {hanso},
Timestamp = {2011.11.17}
}

Tree height, the height from the ground surface to the tree crown, and the crown length as a proportion of tree height of individual trees were derived from various canopy height metrics measured by a small-footprint airborne laser scanner flown over a boreal forest reserve. The average spacing on the ground of the laser pulses ranged from 0.66 to 1.29 m. Ground-truth values were regressed against laser-derived canopy height metrics. The regressions explained 75%, 53%, and 51% of the variability in ground-truth tree height, height to the crown, and relative crown length, respectively. Cross-validation of the regressions revealed standard deviations of the differences between predicted and ground-truth values of 3.15 m (17.6%), 2.19 m (39.1%), and 10.48% (14.9% of ground-truth mean), respectively. On 10 plots with size 50 m2 in the boreal forest reserve and on 27 plots with size 200 m2 in a managed spruce forest, mean tree height, average height from the ground surface to the crown, and average relative crown length were regressed against laser canopy height metrics. The coefficients of determination (R2) ranged from .47 to .91. Cross-validation revealed a precision of 1.49 m (7.6%), 1.24Â¯1.52 m (20.9Â¯23.3%), and 6.32Â¯7.11% (8.8Â¯10.9% of ground-truth mean) for mean tree height, average height to the crown, and average relative crown length, respectively. At least, mean tree height can be determined more accurately from laser data than by current methods.

@Article{Naesset2002,
Title = {Estimating tree height and tree crown properties using airborne scanning laser in a boreal nature reserve},
Author = {Næsset, Erik and Økland, Tonje},
Journal = {Remote Sensing of Environment},
Year = {2002},
Pages = {105-115},
Volume = {79},
Abstract = {Tree height, the height from the ground surface to the tree crown, and the crown length as a proportion of tree height of individual trees were derived from various canopy height metrics measured by a small-footprint airborne laser scanner flown over a boreal forest reserve. The average spacing on the ground of the laser pulses ranged from 0.66 to 1.29 m. Ground-truth values were regressed against laser-derived canopy height metrics. The regressions explained 75%, 53%, and 51% of the variability in ground-truth tree height, height to the crown, and relative crown length, respectively. Cross-validation of the regressions revealed standard deviations of the differences between predicted and ground-truth values of 3.15 m (17.6%), 2.19 m (39.1%), and 10.48% (14.9% of ground-truth mean), respectively. On 10 plots with size 50 m2 in the boreal forest reserve and on 27 plots with size 200 m2 in a managed spruce forest, mean tree height, average height from the ground surface to the crown, and average relative crown length were regressed against laser canopy height metrics. The coefficients of determination (R2) ranged from .47 to .91. Cross-validation revealed a precision of 1.49 m (7.6%), 1.24Â¯1.52 m (20.9Â¯23.3%), and 6.32Â¯7.11% (8.8Â¯10.9% of ground-truth mean) for mean tree height, average height to the crown, and average relative crown length, respectively. At least, mean tree height can be determined more accurately from laser data than by current methods.},
Keywords = {height tree crown laser scanning},
Owner = {hanso},
Timestamp = {2011.11.17}
}

A 20-channel, dual-frequency receiver observing dual-frequency pseudorange and carrier phase of both GPS and GLONASS was used to determine the positional accuracy of 29 points under tree canopies, The mean positional accuracy based on differential postprocessing of GPS+GLONASS single-frequency observations ranged from 0.16 m to 1.16 m for 2.5 min to 20 min of observation at points with basal area ranging from < 20 m(2)/ ha to greater than or equal to 30 m(2)/ ha. The mean positional accuracy of differential postprocessing of dual-frequency GPS+GLONASS observations ranged from 0.08 m to 1.35 m. Using the dual-frequency carrier phase as main observable and fixing the initial integer phase ambiguities, i.e., a fixed solution, gave the best accuracy. However, searching for fixed solutions increased the risk of large individual positional errors due to "false" fixed solutions. The accuracy increased with decreasing density of forest, increasing length of observation period, and decreasing a priori standard error as reported by the postprocessing software.

The mean heights of dominant trees and the stem numbers of 39 plots of 200 m2 each were derived from various canopy height metrics and canopy density measured by means of a small-footprint airborne laser scanner over young forest stands with tree heights <6 m. On the average, the laser transmitted 12,019 pulses ha-1. Ground-truth values were regressed against laser-derived canopy height metrics and density. The regressions explained 83% and 42% of the variability in ground-truth mean height and stem number, respectively. Cross-validation of the regressions revealed standard deviations of the differences between predicted and ground-truth values of mean height and stem number of 0.57 m (15%) and 1209 ha-1 (28.8%), respectively. A proposed practical two-stage procedure for prediction of mean height of dominant trees in forest stands was tested. One hundred and seventy-four sample plots were distributed systematically throughout a 1000-ha forest area. Twenty-nine of the plots were sited in young stands with tree heights <11.5 m. In the first stage, mean height of dominant trees of the 29 plots were regressed against laser-derived canopy height metrics and density. In the second stage, the selected regression was used to predict mean height of 12 selected test stands. The prediction revealed a bias of 0.23 m (3.5%) (P>.05) and a standard deviation of the differences between predicted and ground-truth mean height of 0.56 m (8.4%).

@Article{Naesset2001,
Title = {Estimating tree heights and number of stems in young forest stands using airborne laser scanner data},
Author = {Næsset, Erik and Bjerknes, Kjell-Olav},
Journal = {Remote Sensing of Environment},
Year = {2001},
Pages = {328-340},
Volume = {78},
Abstract = {The mean heights of dominant trees and the stem numbers of 39 plots of 200 m2 each were derived from various canopy height metrics and canopy density measured by means of a small-footprint airborne laser scanner over young forest stands with tree heights <6 m. On the average, the laser transmitted 12,019 pulses ha-1. Ground-truth values were regressed against laser-derived canopy height metrics and density. The regressions explained 83% and 42% of the variability in ground-truth mean height and stem number, respectively. Cross-validation of the regressions revealed standard deviations of the differences between predicted and ground-truth values of mean height and stem number of 0.57 m (15%) and 1209 ha-1 (28.8%), respectively. A proposed practical two-stage procedure for prediction of mean height of dominant trees in forest stands was tested. One hundred and seventy-four sample plots were distributed systematically throughout a 1000-ha forest area. Twenty-nine of the plots were sited in young stands with tree heights <11.5 m. In the first stage, mean height of dominant trees of the 29 plots were regressed against laser-derived canopy height metrics and density. In the second stage, the selected regression was used to predict mean height of 12 selected test stands. The prediction revealed a bias of 0.23 m (3.5%) (P>.05) and a standard deviation of the differences between predicted and ground-truth mean height of 0.56 m (8.4%).},
Keywords = {laser scanning young stands height number of stems},
Owner = {hanso},
Timestamp = {2011.11.17}
}

2000

T. Gobakken, “Economical and biological production possibilities of broadleaves in long term forest production analyses,” PhD Thesis, Ås, 2000. [Bibtex]

A computer program of a forward reaching algorithm of dynamic programming is presented for optimal log bucking. The application is implemented using an object-oriented programming approach. Sensitivity analyses were applied for evaluating the effects in terms of economic value and usable volume, and of altering the price system for saw wood. The data used consisted of 451 Norway spruce (Picea abies (L.) Karst.) stems collected from 13 forest sites located in three regions in Norway. Grade I and Grade II saw wood and pulpwood were used. The mean timber value increased approximately 1% when the new price system was introduced. The value obtained by introducing the new prices varied between the sites (0.1%-1.6%) as well as between regions (0.3%-1.6%). The analyses based on taper equations over-estimated the total value for all the alternatives. Finally. decreasing the width of stem sections and increasing the number of log length alternatives increased the total value of the sample trees but increased the computation time.

@Article{Gobakken2000a,
Title = {The effect of two different price systems on the value and cross-cutting patterns of Norway spruce logs},
Author = {Gobakken, T},
Journal = {Scandinavian Journal of Forest Research},
Year = {2000},
Note = {(03) AGR UNIV NORWAY, DEPT FOREST SCI, POB 5044, N-1432 AS, NORWAY},
Number = {3},
Pages = {368-377},
Volume = {15},
Abstract = {A computer program of a forward reaching algorithm of dynamic programming is presented for optimal log bucking. The application is implemented using an object-oriented programming approach. Sensitivity analyses were applied for evaluating the effects in terms of economic value and usable volume, and of altering the price system for saw wood. The data used consisted of 451 Norway spruce (Picea abies (L.) Karst.) stems collected from 13 forest sites located in three regions in Norway. Grade I and Grade II saw wood and pulpwood were used. The mean timber value increased approximately 1% when the new price system was introduced. The value obtained by introducing the new prices varied between the sites (0.1%-1.6%) as well as between regions (0.3%-1.6%). The analyses based on taper equations over-estimated the total value for all the alternatives. Finally. decreasing the width of stem sections and increasing the number of log length alternatives increased the total value of the sample trees but increased the computation time.},
Keywords = {bucking/optimisation/object-oriented programming/picea abies/bucking},
Owner = {hanso},
Timestamp = {2011.11.17}
}

In order to obtain a more precise prediction of the distribution of each timber grade or log grade with regard to the volume of birch (Betula pendula Roth., B. pubescens Ehrh.) in models for long-term planning, ordered probit models were developed. These models were developed by using data from three mixed birch and Norway spruce stands in Norway. The data consisted of 168 stems. In Norway, three ordinary birch saw log grades are commonly used, with pulpwood as a fourth grade. In this study, these four grades were applied in addition to waste timber, which was treated as a fifth grade. The developed medals showed that the grade distribution of birch trees of mixed birch and spruce stands was highly correlated with tree height (p < 0.01) and height to first visible dry branch (p = 0.081). The statistical significance of both models was good (p < 0.0001), as measured by log likelihood test statistics. Classifying the 168 stems by saw timber or pulpwood in butt log led to greatly improved estimates (p < 0.01). The developed models would allow the incorporation of timber grade in stand simulators, enabling more precise predictions regarding the economic implications of alternative management strategies for birch trees.

@Article{Gobakken2000b,
Title = {Models for assessing timber grade distribution and economic value of standing birch trees},
Author = {Gobakken, T},
Journal = {Scandinavian Journal of Forest Research},
Year = {2000},
Note = {(03) AGR UNIV NORWAY, DEPT FOREST SCI, POB 5044, N-1432 AS, NORWAY},
Number = {5},
Pages = {570-578},
Volume = {15},
Abstract = {In order to obtain a more precise prediction of the distribution of each timber grade or log grade with regard to the volume of birch (Betula pendula Roth., B. pubescens Ehrh.) in models for long-term planning, ordered probit models were developed. These models were developed by using data from three mixed birch and Norway spruce stands in Norway. The data consisted of 168 stems. In Norway, three ordinary birch saw log grades are commonly used, with pulpwood as a fourth grade. In this study, these four grades were applied in addition to waste timber, which was treated as a fifth grade. The developed medals showed that the grade distribution of birch trees of mixed birch and spruce stands was highly correlated with tree height (p < 0.01) and height to first visible dry branch (p = 0.081). The statistical significance of both models was good (p < 0.0001), as measured by log likelihood test statistics. Classifying the 168 stems by saw timber or pulpwood in butt log led to greatly improved estimates (p < 0.01). The developed models would allow the incorporation of timber grade in stand simulators, enabling more precise predictions regarding the economic implications of alternative management strategies for birch trees.},
Keywords = {betula pendula/betula pubescens/ordered probit model/timber grade distribution/silver birch},
Owner = {hanso},
Timestamp = {2011.11.17}
}

A 24-channel, single-frequency receiver observing the C/A code and carrier phase of both GPS and GLONASS was used to determine the positional accuracy of 27 points under tree canopies. The mean positional accuracy based on combined differential postprocessing of GPS C/A code and carrier phase observations ranged from 0.20 m to 5.72 m for 2.5 min to 30 min of observation at points with basal area ranging from 2/ha to >=25 m2/ha. The mean positional accuracy of combined differential postprocessing of GPS+GLONASS C/A code and carrier phase observations ranged from 0.09 m to 2.85 m. The accuracy increased with decreasing density of forest and improving geometric satellite distribution as observed at the base station. Static differential postprocessing of 30-min observations of GPS+GLONASS carrier phase gave fixed solutions for 13 of the 27 points. The accuracy of these solutions ranged from 0.01 m to 0.09 m. The probability of obtaining a fixed solution under tree canopies was modeled by logistic regression. The regression model classified correctly 81.5 percent of the processed solutions. [Journal Review; 13 Refs; In English; Summary in English]

@Article{Naesset2000,
Title = {Contributions of differential GPS and GLONASS observations to point accuracy under forest canopies},
Author = {Næsset, E. and Bjerke, T. and Ovstedal, O. and Ryan, L.H.},
Journal = {Photogrammetric Engineering and Remote Sensing},
Year = {2000},
Note = {TY - JOUR},
Number = {4},
Pages = {403-407},
Volume = {66},
Abstract = {A 24-channel, single-frequency receiver observing the C/A code and carrier phase of both GPS and GLONASS was used to determine the positional accuracy of 27 points under tree canopies. The mean positional accuracy based on combined differential postprocessing of GPS C/A code and carrier phase observations ranged from 0.20 m to 5.72 m for 2.5 min to 30 min of observation at points with basal area ranging from 2/ha to >=25 m2/ha. The mean positional accuracy of combined differential postprocessing of GPS+GLONASS C/A code and carrier phase observations ranged from 0.09 m to 2.85 m. The accuracy increased with decreasing density of forest and improving geometric satellite distribution as observed at the base station. Static differential postprocessing of 30-min observations of GPS+GLONASS carrier phase gave fixed solutions for 13 of the 27 points. The accuracy of these solutions ranged from 0.01 m to 0.09 m. The probability of obtaining a fixed solution under tree canopies was modeled by logistic regression. The regression model classified correctly 81.5 percent of the processed solutions. [Journal Review; 13 Refs; In English; Summary in English]},
Owner = {hanso},
Timestamp = {2011.11.17}
}

A material of eight individual mature stands of different sizes and shapes and with different types of neighbouring stands was used to assess the effect of erroneous placement of stand boundaries by photointerpretation on the estimated area of individual stands. The boundaries of the eight stands were classified into three different groups according to the length of the shadows caused by the trees in the mature stands along the boundaries. Different distributions of errors in stand boundaries were assigned to the three groups of boundaries. By means of Monte Carlo simulation of errors in each boundary, the placement of each boundary was altered according to a normally distributed random deviate. New stand polygons were created and their areas were estimated. A total of 850 independent areal estimates was produced for each of the eight stands. The difference between mean area of the simulations and true area ranged from -1.3% to -9.6% of true area. The standard deviation for the simulated area ranged from 1.5% to 9.0%.

@Article{Naesset1999,
Title = {Assessing the effect of erroneous placement of forest stand boundaries on the estimated area of individual stands},
Author = {Næsset, E.},
Journal = {Scandinavian Journal of Forest Research},
Year = {1999},
Note = {TY - JOUR},
Number = {2},
Pages = {175-181},
Volume = {14},
Abstract = {A material of eight individual mature stands of different sizes and shapes and with different types of neighbouring stands was used to assess the effect of erroneous placement of stand boundaries by photointerpretation on the estimated area of individual stands. The boundaries of the eight stands were classified into three different groups according to the length of the shadows caused by the trees in the mature stands along the boundaries. Different distributions of errors in stand boundaries were assigned to the three groups of boundaries. By means of Monte Carlo simulation of errors in each boundary, the placement of each boundary was altered according to a normally distributed random deviate. New stand polygons were created and their areas were estimated. A total of 850 independent areal estimates was produced for each of the eight stands. The difference between mean area of the simulations and true area ranged from -1.3% to -9.6% of true area. The standard deviation for the simulated area ranged from 1.5% to 9.0%.},
Doi = {10.1080/02827589950152908},
Keywords = {error assessment forest survey gis monte carlo simulation stand area},
Owner = {hanso},
Timestamp = {2011.11.17},
Url = {http://www.tandfonline.com/doi/abs/10.1080/02827589950152908}
}

Decomposition rate constants were estimated from 384 cross sections of Norway spruce (Picea abies (L.) Karat.) logs with base diameter >7.0 cm collected in open areas at five different study sites in southeastern Norway. Fresh wood core samples were taken from 95 standing trees adjacent to the logs to estimate the initial density of these cross sections. Based on this chronosequence, a simple negative exponential function of time showed an average decomposition rate constant for all cross sections of 0.033 per year. Cross-section diameter, ground contact, soil moisture, and aspect were all found to have significant impacts on the decomposition rate constant. For different combinations of these characteristics the decomposition rate constant ranged from a minimum of 0.0165 per year to a maximum of 0.0488 per year.

@Article{Naesset1999a,
Title = {Decomposition rate constants of Picea abies logs in southeastern Norway},
Author = {Næsset, E.},
Journal = {Canadian journal of forest research- Revue canadienne de recherche forestier},
Year = {1999},
Note = {TY - JOUR},
Number = {3},
Pages = {372-381},
Volume = {29},
Abstract = {Decomposition rate constants were estimated from 384 cross sections of Norway spruce (Picea abies (L.) Karat.) logs with base diameter >7.0 cm collected in open areas at five different study sites in southeastern Norway. Fresh wood core samples were taken from 95 standing trees adjacent to the logs to estimate the initial density of these cross sections. Based on this chronosequence, a simple negative exponential function of time showed an average decomposition rate constant for all cross sections of 0.033 per year. Cross-section diameter, ground contact, soil moisture, and aspect were all found to have significant impacts on the decomposition rate constant. For different combinations of these characteristics the decomposition rate constant ranged from a minimum of 0.0165 per year to a maximum of 0.0488 per year.},
Keywords = {coarse woody debris douglas-fir forests dead wood spruce bryophytes dynamics oregon},
Owner = {hanso},
Timestamp = {2011.11.17}
}

The relationship between relative density and simple classification systems of decayed coarse woody debris was assessed for 384 cross-sections of decayed Norway spruce [Picea abies (L.) Karst.] logs collected at five different study sites in south-eastern Norway. The relative density was computed as the ratio of actual density to the density of fresh wood. Three different classification systems were tested. One consisted of five classes and the other two consisted of eight classes each. All of the systems classified the samples according to variables related to bark. wood and trunk shape. The Spearman rank correlation coefficient between relative density and decay class ranged from – 0.77 to – 0.80. The correlation coefficients of the three classifications did not differ significantly from each other. The mean relative density within each decay class also decreased successively from one decay class to the next for all classification systems. For the five-class system the mean relative density decreased from 0.97 in the least decayed class to 0.28 in the most decayed class. For the two eight-class systems the mean relative density decreased from 0.94 to 0.39 and from 0.97 to 0.24, respectively.

@Article{Naesset1999b,
Title = {Relationship between relative wood density of Picea abies logs and simple classification systems of decayed coarse woody debris},
Author = {Næsset, E.},
Journal = {Scandinavian Journal of Forest Research},
Year = {1999},
Note = {TY - JOUR},
Number = {5},
Pages = {454-461},
Volume = {14},
Abstract = {The relationship between relative density and simple classification systems of decayed coarse woody debris was assessed for 384 cross-sections of decayed Norway spruce [Picea abies (L.) Karst.] logs collected at five different study sites in south-eastern Norway. The relative density was computed as the ratio of actual density to the density of fresh wood. Three different classification systems were tested. One consisted of five classes and the other two consisted of eight classes each. All of the systems classified the samples according to variables related to bark. wood and trunk shape. The Spearman rank correlation coefficient between relative density and decay class ranged from - 0.77 to - 0.80. The correlation coefficients of the three classifications did not differ significantly from each other. The mean relative density within each decay class also decreased successively from one decay class to the next for all classification systems. For the five-class system the mean relative density decreased from 0.97 in the least decayed class to 0.28 in the most decayed class. For the two eight-class systems the mean relative density decreased from 0.94 to 0.39 and from 0.97 to 0.24, respectively.},
Keywords = {dead wood decay rate forest survey norway spruce olympic national-park douglas-fir forests northern sweden decomposition washington patterns oregon},
Owner = {hanso},
Timestamp = {2011.11.17}
}

One 6-channel and two 12-channel single frequency GPS receivers observing C/A (course/acquisition) code and carrier phase were tested to determine positional point accuracies under conifer and deciduous tree canopies. Positional accuracies were determined for 38 subcanopy sites by differential processing of C/A code observations only and combined use of C/A code and carrier phase. Observation periods of 2.5-30 min were evaluated. Mean positional accuracy ranged from 1.17 to 3.70 m for the 12-channel receivers based on 2.5-30 min of observation of C/A code. Mean accuracy ranged from 0.79 to 2.25 m for combined use of C/A code and carrier phase. The accuracy was 7.34 m with 30 min of C/A code observations with the 6-channel receiver. With combined use of C/A code and carrier phase the mean accuracy of the 6-channel receiver increased to 0.98-2.44 m. The accuracy increased with decreasing basal area and improving geometric satellite distribution. The mean accuracy was significantly higher for the 12-channel receivers than for the 6-channel receiver and significantly higher by combined use of C/A code and carrier phase than use of C/A code only.

@Article{Naesset1999e,
Title = {Point accuracy of combined pseudorange and carrier phase differential GPS under forest canopy},
Author = {Næsset, E.},
Journal = {Canadian journal of forest research- Revue canadienne de recherche forestier},
Year = {1999},
Note = {TY - JOUR},
Number = {5},
Pages = {547-553},
Volume = {29},
Abstract = {One 6-channel and two 12-channel single frequency GPS receivers observing C/A (course/acquisition) code and carrier phase were tested to determine positional point accuracies under conifer and deciduous tree canopies. Positional accuracies were determined for 38 subcanopy sites by differential processing of C/A code observations only and combined use of C/A code and carrier phase. Observation periods of 2.5-30 min were evaluated. Mean positional accuracy ranged from 1.17 to 3.70 m for the 12-channel receivers based on 2.5-30 min of observation of C/A code. Mean accuracy ranged from 0.79 to 2.25 m for combined use of C/A code and carrier phase. The accuracy was 7.34 m with 30 min of C/A code observations with the 6-channel receiver. With combined use of C/A code and carrier phase the mean accuracy of the 6-channel receiver increased to 0.98-2.44 m. The accuracy increased with decreasing basal area and improving geometric satellite distribution. The mean accuracy was significantly higher for the 12-channel receivers than for the 6-channel receiver and significantly higher by combined use of C/A code and carrier phase than use of C/A code only.},
Owner = {hanso},
Timestamp = {2011.11.17}
}

A 717 ha forest area dominated by Norway spruce [Picea abies (L.) Karst.] and Scots pine [Pinus sylvestris L.] was used to assess the effects of photointerpreter errors in the placement of stand boundaries on the estimated area of different groups of forest stands (strata) and other land use classes, and on the total timber volume estimate of the forest. The boundaries between thinning phase stands and clearcuts were classified into three different groups according to the length of the tree shadows hindering ground visibility in the aerial photographs. Different distributions of errors in stand boundaries were assigned to the three groups of boundaries. By means of Monte Carlo simulations of errors in each boundary, the placement of each boundary was altered according to a normally distributed random deviate. In total. 125 independent estimates of total timber volume and area of different land use classes were produced. For thinning phase forest, the difference between the mean area of the simulations and true area was -2.0% of true area. The corresponding difference for total timber volume of all stands was -2.1%. For individual simulations, the minimum and maximum biases in timber volume were -4.8% and 3.1%, respectively.

@Article{Naesset1999f,
Title = {Effects of delineation errors in forest stand boundaries on estimated area and timber volumes},
Author = {Næsset, E.},
Journal = {Scandinavian Journal of Forest Research},
Year = {1999},
Note = {TY - JOUR},
Number = {6},
Pages = {558-566},
Volume = {14},
Abstract = {A 717 ha forest area dominated by Norway spruce [Picea abies (L.) Karst.] and Scots pine [Pinus sylvestris L.] was used to assess the effects of photointerpreter errors in the placement of stand boundaries on the estimated area of different groups of forest stands (strata) and other land use classes, and on the total timber volume estimate of the forest. The boundaries between thinning phase stands and clearcuts were classified into three different groups according to the length of the tree shadows hindering ground visibility in the aerial photographs. Different distributions of errors in stand boundaries were assigned to the three groups of boundaries. By means of Monte Carlo simulations of errors in each boundary, the placement of each boundary was altered according to a normally distributed random deviate. In total. 125 independent estimates of total timber volume and area of different land use classes were produced. For thinning phase forest, the difference between the mean area of the simulations and true area was -2.0% of true area. The corresponding difference for total timber volume of all stands was -2.1%. For individual simulations, the minimum and maximum biases in timber volume were -4.8% and 3.1%, respectively.},
Keywords = {error assessment forest survey gis monte carlo simulation monte-carlo simulation photo-interpretation},
Owner = {hanso},
Timestamp = {2011.11.17}
}

A material of 615 observations was used to develop Norway spruce (Picea abies (L.) Karst.) stand volume Functions for eastern, central and northern Norway. A multiplicative model with three independent variables was found to be most suitable. The independent variables were stand basal area. Lorey’s mean height, and site index. The R-2 value was 0.993 and the coefficient of variation 5.43%. Testing by means of two independent data sets indicated that the function is suitable for practical prediction purposes for different site qualities and in different geographic regions of the country.

@Article{Naesset1999c,
Title = {Stand volume functions for Picea abies in eastern, central and northern Norway},
Author = {Næsset, E. and Tveite, B.},
Journal = {Scandinavian Journal of Forest Research},
Year = {1999},
Note = {TY - JOUR},
Number = {2},
Pages = {164-174},
Volume = {14},
Abstract = {A material of 615 observations was used to develop Norway spruce (Picea abies (L.) Karst.) stand volume Functions for eastern, central and northern Norway. A multiplicative model with three independent variables was found to be most suitable. The independent variables were stand basal area. Lorey's mean height, and site index. The R-2 value was 0.993 and the coefficient of variation 5.43%. Testing by means of two independent data sets indicated that the function is suitable for practical prediction purposes for different site qualities and in different geographic regions of the country.},
Keywords = {basal area mean height picea abies site index stand volume},
Owner = {hanso},
Timestamp = {2011.11.17}
}

The accuracy of determination of stand volume (m(3) ha(-1)) using two practical survey methods, i.e. relascope survey and photo-interpretation, was tested on 333 forest stands of Norway spruce (Picea abies (L.) Karst.), Scots pine (Pinus sylvestris L.), and mixed stands of spruce, pine, and deciduous tree species. The material was collected at 14 different test sites in southern Norway and between 1 and 19 individual surveyors participated at each site. Reference values for stand volume of each stand were obtained from intensive field measurements. On average, the relascope surveyors underestimated the reference volume by 2% to 6%. The average standard deviation for the differences between practically determined volume and reference volume was in the range 15-31%. For stand volume determined by photo-interpretation, an average underestimation of reference volume of 4% to 38% was found. The average standard deviation for the differences was in the range 13-33%. In the relascope surveys the practically determined stand volume was predicted from mean height and basal area. On average, the effect on stand volume of bias in the stand height measurements and in the basal area measurements seemed to be equally serious.

@Article{Eid1998,
Title = {Determination of stand volume in practical forest inventories based on field measurements and photo-interpretation: the Norwegian experience},
Author = {Eid, T. and Næsset, E.},
Journal = {Scandinavian Journal of Forest Research},
Year = {1998},
Note = {TY - JOUR},
Number = {2},
Pages = {246-254},
Volume = {13},
Abstract = {The accuracy of determination of stand volume (m(3) ha(-1)) using two practical survey methods, i.e. relascope survey and photo-interpretation, was tested on 333 forest stands of Norway spruce (Picea abies (L.) Karst.), Scots pine (Pinus sylvestris L.), and mixed stands of spruce, pine, and deciduous tree species. The material was collected at 14 different test sites in southern Norway and between 1 and 19 individual surveyors participated at each site. Reference values for stand volume of each stand were obtained from intensive field measurements. On average, the relascope surveyors underestimated the reference volume by 2% to 6%. The average standard deviation for the differences between practically determined volume and reference volume was in the range 15-31%. For stand volume determined by photo-interpretation, an average underestimation of reference volume of 4% to 38% was found. The average standard deviation for the differences was in the range 13-33%. In the relascope surveys the practically determined stand volume was predicted from mean height and basal area. On average, the effect on stand volume of bias in the stand height measurements and in the basal area measurements seemed to be equally serious.},
Doi = {10.1080/02827589809382982},
Keywords = {aerial photographs photo-interpretation relascope survey stand inventory stand volume sammenlignet med laser scanning},
Owner = {hanso},
Timestamp = {2011.11.17},
Url = {http://www.tandfonline.com/doi/abs/10.1080/02827589809382982}
}

Four skilled interpreters delineated 48 boundaries between mature forest stands and clear-felled or nonproductive areas using black and white and color infrared aerial photographs. The positions of the boundaries were compared with ground-truth points located along the boundaries. On average, the interpreters located the boundaries 1.22-2.34 m inside the mature forest stands. Film type, focal length, location of the boundaries within different parts of the stereo models, and the height of the trees along the boundaries did not affect the interpretations significantly. Shadows caused by the trees, which is a hinderance to ground visibility, seriously affected the interpretation. The interpreters placed the boundaries 0.62 m farther inside the mature stands for boundaries covered by shadow than for boundaries without shadows.

@Article{Naesset1998,
Title = {Positional accuracy of boundaries between clearcuts and mature forest stands delineated by means of aerial photointerpretation},
Author = {Næsset, E.},
Journal = {Canadian journal of forest research- Revue canadienne de recherche forestier},
Year = {1998},
Note = {TY - JOUR},
Number = {3},
Pages = {368-374},
Volume = {28},
Abstract = {Four skilled interpreters delineated 48 boundaries between mature forest stands and clear-felled or nonproductive areas using black and white and color infrared aerial photographs. The positions of the boundaries were compared with ground-truth points located along the boundaries. On average, the interpreters located the boundaries 1.22-2.34 m inside the mature forest stands. Film type, focal length, location of the boundaries within different parts of the stereo models, and the height of the trees along the boundaries did not affect the interpretations significantly. Shadows caused by the trees, which is a hinderance to ground visibility, seriously affected the interpretation. The interpreters placed the boundaries 0.62 m farther inside the mature stands for boundaries covered by shadow than for boundaries without shadows.},
Owner = {hanso},
Timestamp = {2011.11.17}
}

1997

Mean tree height of 36 test stands is derived from tree canopy heights measured by means of an airborne laser scanner. On the average the laser recorded 505-1070 canopy heights per stand. First, the laser mean height is computed as the arithmetic mean of the canopy heights within each stand. The laser mean height underestimates the ground truth mean height by 4.1-5.5 m. Second, a weighted mean of the laser canopy heights is computed. The individual height values are used as weights. The weighted mean height underestimates the true height by 2.1-3.6 m. Finally, the laser mean height is computed as the arithmetic mean of the largest laser values within square grid cells with cell sizes of 15-30 m. The bias of the laser estimates is in the range -0.4 m to 1.9 m. The standard deviation for differences between the laser mean heights and the ground truth mean height is 1.1-1.6 m. [Journal Article; In English]

@Article{Naesset1997,
Title = {Determination of mean tree height of forest stands using airborne laser scanner data},
Author = {Næsset, E.},
Journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
Year = {1997},
Note = {TY - JOUR},
Pages = {49-56},
Volume = {52},
Abstract = {Mean tree height of 36 test stands is derived from tree canopy heights measured by means of an airborne laser scanner. On the average the laser recorded 505-1070 canopy heights per stand. First, the laser mean height is computed as the arithmetic mean of the canopy heights within each stand. The laser mean height underestimates the ground truth mean height by 4.1-5.5 m. Second, a weighted mean of the laser canopy heights is computed. The individual height values are used as weights. The weighted mean height underestimates the true height by 2.1-3.6 m. Finally, the laser mean height is computed as the arithmetic mean of the largest laser values within square grid cells with cell sizes of 15-30 m. The bias of the laser estimates is in the range -0.4 m to 1.9 m. The standard deviation for differences between the laser mean heights and the ground truth mean height is 1.1-1.6 m. [Journal Article; In English]},
Keywords = {forest inventory laser scanning tree heights},
Owner = {hanso},
Timestamp = {2011.11.17}
}

The stand volumes of 36 Norway spruce (Picea abies Karst.) and Scots pine (Pinus sylvestris L.) stands were derived from various free canopy height metrics and canopy cover density measured by means of an airborne laser scanner. On average, the laser transmitted 1350-1910 poises per stand and recorded 505-1070 canopy hits with corresponding estimates of canopy height. Ground truth stand volume was regressed against mean stand height, the mean height of all laser pulses within a stand, and canopy cover density as determined from the laser data. The coefficients of determination were in the range between 0.456 and 0.887. The coefficients of variation ranged from 17.2% to 43.3%. (C) Elsevier Science Inc., 1997.

In long-term forest management, aspatial models for economic optimization of timber production have proven to be efficient means in the management planning process. The increased concern for preservation of biodiversity increases the complexity of the forest management planning and new tools are needed in order to accomplish planning comprising objectives related to biodiversity and economic timber production. These needs coincide with the development of geographical information systems (GIS). GIS may in several ways contribute to a further development of management planning routines and tools that incorporates biodiversity goals and constraints. In this article, how GIS can be used to search for sensitive areas that should be devoted to careful timber management practices, and how the technology could be used to model phenomena of ecological significance is discussed. Special attention is devoted to the integration of GIS with quantitative models for long-term forest management planning and several current applications are reviewed. A methodology for forest management planning promoting the preservation of biodiversity is proposed and demonstrated. (C) 1997 Elsevier Science B.V.

@Article{Naesset1997b,
Title = {Geographical information systems in long-term forest management and planning with special reference to preservation of biological diversity: A review},
Author = {Næsset, E.},
Journal = {Forest Ecology and Management},
Year = {1997},
Note = {TY - JOUR},
Pages = {121-136},
Volume = {93},
Abstract = {In long-term forest management, aspatial models for economic optimization of timber production have proven to be efficient means in the management planning process. The increased concern for preservation of biodiversity increases the complexity of the forest management planning and new tools are needed in order to accomplish planning comprising objectives related to biodiversity and economic timber production. These needs coincide with the development of geographical information systems (GIS). GIS may in several ways contribute to a further development of management planning routines and tools that incorporates biodiversity goals and constraints. In this article, how GIS can be used to search for sensitive areas that should be devoted to careful timber management practices, and how the technology could be used to model phenomena of ecological significance is discussed. Special attention is devoted to the integration of GIS with quantitative models for long-term forest management planning and several current applications are reviewed. A methodology for forest management planning promoting the preservation of biodiversity is proposed and demonstrated. (C) 1997 Elsevier Science B.V.},
Keywords = {biodiversity forest management geographical information system gis spatial decision support system decision-support system discriminant function-analysis ecosystem management landscape patterns gis model habitat plans constraints timber},
Owner = {hanso},
Timestamp = {2011.11.17}
}

The increased concern for environmental values increases the complexity of long-term forest management planning. Environmental issues have to be dealt with over space and time. By creating links between existing aspatial long-term forest management models and geographical information systems (GIS), environmental issues can be treated within the forest management planning process. In this article, a spatial decision support system (SDSS) developed by incorporating a forest management planning model into a GIS is presented. A case study is employed to illustrate the usefulness of the SDSS using real data. In order to preserve the water bodies in a forest area, the allowable treatments in areas falling within certain distances of lakes, streams, and swamps were restricted. Treatment schedules were simulated for all stands, and linear programming was utilized to maximize the net present value (NPV) subject to a non-declining felling path. The NPV was reduced by 6.9% due to the treatment restrictions, and the annual harvest flow was reduced by about 10%. Various thematic maps of future time periods may be produced for subjective evaluation of the results.

@Article{Naesset1997c,
Title = {A spatial decision support system for long-term forest management planning by means of linear programming and a geographical information system},
Author = {Næsset, E.},
Journal = {Scandinavian Journal of Forest Research},
Year = {1997},
Note = {TY - JOUR},
Pages = {77-88},
Volume = {12},
Abstract = {The increased concern for environmental values increases the complexity of long-term forest management planning. Environmental issues have to be dealt with over space and time. By creating links between existing aspatial long-term forest management models and geographical information systems (GIS), environmental issues can be treated within the forest management planning process. In this article, a spatial decision support system (SDSS) developed by incorporating a forest management planning model into a GIS is presented. A case study is employed to illustrate the usefulness of the SDSS using real data. In order to preserve the water bodies in a forest area, the allowable treatments in areas falling within certain distances of lakes, streams, and swamps were restricted. Treatment schedules were simulated for all stands, and linear programming was utilized to maximize the net present value (NPV) subject to a non-declining felling path. The NPV was reduced by 6.9% due to the treatment restrictions, and the annual harvest flow was reduced by about 10%. Various thematic maps of future time periods may be produced for subjective evaluation of the results.},
Keywords = {gis lp strategic planning treatment-schedule simulation ecosystem management harvest constraints timber plans},
Owner = {hanso},
Timestamp = {2011.11.17}
}

The effects, in terms of economic value and harvest potential, of adopting restricted timber management practices promoting the preservation of biodiversity are analysed in a 5620 ha boreal forest landscape in southeast Norway. A forest management model based on ecological principles aiming at preservation of biodiversity, e.g. by adopting restricted silvicultural treatments imitating the effects of natural processes, such as forest fires, is applied. The forest area is divided into four separate classes reflecting the probabilities of forest fires, and different treatment options are assigned to each of the classes. These options comprise extended rotation periods and treatments such as clear-cutting with retention of seed trees and shelterwood cutting. A management problem specified as maximization of net present value subject to a requirement to preserve (medium term) about 10% of the forest area for a period of 70 yrs is solved by means of linear programming. Compared with current timber management practices, the net present value of the restricted management problem is reduced by 14% (15%) for a real rate of discount of 3% (4%) pro anno. The annual harvest flow is reduced by 11-33%. For a management problem comprising even-flow harvesting constraints in addition to biodiversity constraints, the net present value is reduced by 16% (19%).

@Article{Naesset1997d,
Title = {Economic analysis of timber management practices promoting preservation of biological diversity},
Author = {Næsset, Erik and Gobakken, Terje and Hoen, Hans Fredrik},
Journal = {Scandinavian Journal of Forest Research},
Year = {1997},
Note = {(03) AGR UNIV NORWAY, DEPT FOREST SCI, POB 5044, N-1432 AS, NORWAY},
Number = {3},
Pages = {264-272},
Volume = {12},
Abstract = {The effects, in terms of economic value and harvest potential, of adopting restricted timber management practices promoting the preservation of biodiversity are analysed in a 5620 ha boreal forest landscape in southeast Norway. A forest management model based on ecological principles aiming at preservation of biodiversity, e.g. by adopting restricted silvicultural treatments imitating the effects of natural processes, such as forest fires, is applied. The forest area is divided into four separate classes reflecting the probabilities of forest fires, and different treatment options are assigned to each of the classes. These options comprise extended rotation periods and treatments such as clear-cutting with retention of seed trees and shelterwood cutting. A management problem specified as maximization of net present value subject to a requirement to preserve (medium term) about 10% of the forest area for a period of 70 yrs is solved by means of linear programming. Compared with current timber management practices, the net present value of the restricted management problem is reduced by 14% (15%) for a real rate of discount of 3% (4%) pro anno. The annual harvest flow is reduced by 11-33%. For a management problem comprising even-flow harvesting constraints in addition to biodiversity constraints, the net present value is reduced by 16% (19%).},
Keywords = {biodiversity/gis/linear programming/strategic planning/treatment schedule simulation/decision-support system/ecosystem management/short-term/forest/constraints/plans},
Owner = {hanso},
Timestamp = {2011.11.17}
}

The weighted Kappa coefficient is applied to the comparison of thematic maps. Weighted Kappa is a useful measure of accuracy when the map classes are ordered, or when the relative seriousness of the different possible errors may vary. The calculation and interpretation of weighted Kappa are demonstrated by two examples from forest surveys. First, the accuracy of thematic site quality maps classified according to an ordinal scale is assessed. Error matrices are derived from map overlays, and two different sets of agreement weights are used for the calculation. Weighted Kappa ranges from 0.34 to 0.55, but it does not differ significantly between two separate areas. Secondly, weighted Kappa is calculated for a tree species cover classified according to a nominal scale. Weights reflecting the economic loss for the forest owner due to erroneous data are used for the computation. The value of weighted Kappa is 0.56.

@Article{Naesset1996,
Title = {Use of the weighted Kappa coefficient in classification error assessment of thematic maps},
Author = {Næsset, E.},
Journal = {International Journal of Geographical Information Systems},
Year = {1996},
Note = {TY - JOUR},
Number = {5},
Pages = {591-603},
Volume = {10},
Abstract = {The weighted Kappa coefficient is applied to the comparison of thematic maps. Weighted Kappa is a useful measure of accuracy when the map classes are ordered, or when the relative seriousness of the different possible errors may vary. The calculation and interpretation of weighted Kappa are demonstrated by two examples from forest surveys. First, the accuracy of thematic site quality maps classified according to an ordinal scale is assessed. Error matrices are derived from map overlays, and two different sets of agreement weights are used for the calculation. Weighted Kappa ranges from 0.34 to 0.55, but it does not differ significantly between two separate areas. Secondly, weighted Kappa is calculated for a tree species cover classified according to a nominal scale. Weights reflecting the economic loss for the forest owner due to erroneous data are used for the computation. The value of weighted Kappa is 0.56.},
Doi = {10.1080/02693799608902099},
Keywords = {remotely sensed data accuracy agreement consistency},
Owner = {hanso},
Timestamp = {2011.11.17},
Url = {http://www.tandfonline.com/doi/abs/10.1080/02693799608902099}
}

The error matrix is frequently used for accuracy assessment of the classification of remotely sensed data. The classification accuracy for each individual category is often expressed by the percentage correct classified and/or the conditional kappa coefficient. In the classification of remotely sensed data, the distribution of the reference data over various categories is often unknown beforehand. In such cases, conditional kappa may give erroneous estimates of classification accuracy. An alternative measure for classification accuracy of individual categories is proposed. It is denoted as conditional tau, and it expresses the agreement obtained after removal of the random agreement expected by chance. Formulae for computation of conditional tau appropriate for classifications based on equal and unequal probabilities of category membership are presented. Three numerical examples are used to demonstrate the calculation and interpretation of the measure. In the examples, conditional tau correctly estimates the classification accuracy, whilst the results show that conditional kappa may overestimate as well as underestimate the classification accuracy.

@Article{Naesset1996a,
Title = {Conditional tau coefficient for assessment of producer's accuracy of classified remotely sensed data},
Author = {Næsset, Erik},
Journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
Year = {1996},
Note = {TY - JOUR},
Number = {2},
Pages = {91-98},
Volume = {51},
Abstract = {The error matrix is frequently used for accuracy assessment of the classification of remotely sensed data. The classification accuracy for each individual category is often expressed by the percentage correct classified and/or the conditional kappa coefficient. In the classification of remotely sensed data, the distribution of the reference data over various categories is often unknown beforehand. In such cases, conditional kappa may give erroneous estimates of classification accuracy. An alternative measure for classification accuracy of individual categories is proposed. It is denoted as conditional tau, and it expresses the agreement obtained after removal of the random agreement expected by chance. Formulae for computation of conditional tau appropriate for classifications based on equal and unequal probabilities of category membership are presented. Three numerical examples are used to demonstrate the calculation and interpretation of the measure. In the examples, conditional tau correctly estimates the classification accuracy, whilst the results show that conditional kappa may overestimate as well as underestimate the classification accuracy.},
Owner = {hanso},
Timestamp = {2011.11.17}
}

A procedure for calculation of stumpage value and logging costs of individual mature forest stands using aerial photo-interpretation and a grid-based geographical information system (GIS) is presented. The stumpage value may be computed from characteristics related to the trees, i.e. site index, stand mean height, crown closure, and tree species distribution, using price equations. By means of cost equations, the logging costs may be calculated from the tree characteristics and the terrain characteristics of dope gradient and skidding distance. ?The practical application of the procedure was demonstrated by a case study in a 710 ha forest area in southern Norway. The tree characteristics were determined by photo-interpretation of individual stands. Skid paths for wood transportation from the stands to landings along the forest roads were delineated by photo-interpretation of the ground conditions. Slope and skidding distances were derived by a digital elevation model and cartographic meddling. Finally, the photo-interpreted tree characteristics and the computed slope and skidding distances were used for calculation of the stumpage value and the logging costs of each stand. According to previous tests, the accuracy of the procedure corresponded to the accuracy that could be achieved by the held-survey methods used most frequently.

@Article{Naesset1996b,
Title = {Derivation of stumpage value and logging costs for individual forest stands using aerial photographs and cartographic modelling},
Author = {Næsset, E.},
Journal = {Scandinavian Journal of Forest Research},
Year = {1996},
Note = {TY - JOUR},
Number = {4},
Pages = {388-396},
Volume = {11},
Abstract = {A procedure for calculation of stumpage value and logging costs of individual mature forest stands using aerial photo-interpretation and a grid-based geographical information system (GIS) is presented. The stumpage value may be computed from characteristics related to the trees, i.e. site index, stand mean height, crown closure, and tree species distribution, using price equations. By means of cost equations, the logging costs may be calculated from the tree characteristics and the terrain characteristics of dope gradient and skidding distance. ?The practical application of the procedure was demonstrated by a case study in a 710 ha forest area in southern Norway. The tree characteristics were determined by photo-interpretation of individual stands. Skid paths for wood transportation from the stands to landings along the forest roads were delineated by photo-interpretation of the ground conditions. Slope and skidding distances were derived by a digital elevation model and cartographic meddling. Finally, the photo-interpreted tree characteristics and the computed slope and skidding distances were used for calculation of the stumpage value and the logging costs of each stand. According to previous tests, the accuracy of the procedure corresponded to the accuracy that could be achieved by the held-survey methods used most frequently.},
Keywords = {cartographic modelling digital elevation model forest management planning data geographical information system photo-interpretation stand inventory interpolation},
Owner = {hanso},
Timestamp = {2011.11.17}
}

Two models for determination of the number of stems per hectare in forest stands (N) from attributes derived by aerial photo-interpretation were developed. The models relied on the assumption that N could be determined by dividing the total stand volume per hectare with the volume of the ”average tree” defined by stand mean height and the diameter corresponding to mean basal area of a stand. Input variables of the models were stand mean height, crown closure and site quality. Additionally, model II required input of average stand volume per hectare and average mean diameter derived from stratified field sample plot inventories. Material for 143 coniferous stands was used for the testing of the models. The stands were recorded by intensive field measurements. Aerial photographs at the approximate scale of 1 : 15000 were used for photo-interpretation. The N value was underestimated in model I by 5.4-47.0%. The standard deviation for the differences was 15.2-26.2% for mature stands and 41.4-44.2% for young thinning phase stands. For model II, the mean difference between the predicted and observed N value was in the range-16.1% to 12.2%.

@Article{Naesset1996c,
Title = {Determination of number of stems in coniferous forest stands by means of aerial photo-interpretation},
Author = {Næsset, E.},
Journal = {Scandinavian Journal of Forest Research},
Year = {1996},
Note = {TY - JOUR},
Number = {1},
Pages = {76-84},
Volume = {11},
Abstract = {Two models for determination of the number of stems per hectare in forest stands (N) from attributes derived by aerial photo-interpretation were developed. The models relied on the assumption that N could be determined by dividing the total stand volume per hectare with the volume of the ''average tree'' defined by stand mean height and the diameter corresponding to mean basal area of a stand. Input variables of the models were stand mean height, crown closure and site quality. Additionally, model II required input of average stand volume per hectare and average mean diameter derived from stratified field sample plot inventories. Material for 143 coniferous stands was used for the testing of the models. The stands were recorded by intensive field measurements. Aerial photographs at the approximate scale of 1 : 15000 were used for photo-interpretation. The N value was underestimated in model I by 5.4-47.0%. The standard deviation for the differences was 15.2-26.2% for mature stands and 41.4-44.2% for young thinning phase stands. For model II, the mean difference between the predicted and observed N value was in the range-16.1% to 12.2%.},
Keywords = {aerial photographs mean diameter number of stems photo-interpretation stand inventory},
Owner = {hanso},
Timestamp = {2011.11.17}
}

One model for determination of stumpage value per cubic metre and two different models for determination of logging costs per cubic metre of final fellings were developed by using attributes derived by aerial photo-interpretation. Input variables of the models were stand mean height, crown closure, site quality, tree species composition, slope, and skidding distance, and average number of trees per cubic metre and average tariff number derived from a stratified field sample plot inventory. Intensive field measurements of 119 mature coniferous stands were used to test the models. Aerial photographs at the approximate scale of 1 : 15 000 were used for photo-interpretation. The bias of the estimated stumpage value was in the range -3.3% to 5.2%. The standard deviation was 2.3-10.8%. For the estimated logging costs the bias was in the range -10.5% to 2.9%. The standard deviation was 4.2-11.7%.

@Article{Naesset1996d,
Title = {Determination of stumpage value and logging costs in coniferous forest stands by means of aerial photo-interpretation},
Author = {Næsset, E.},
Journal = {Scandinavian Journal of Forest Research},
Year = {1996},
Note = {TY - JOUR},
Number = {3},
Pages = {291-299},
Volume = {11},
Abstract = {One model for determination of stumpage value per cubic metre and two different models for determination of logging costs per cubic metre of final fellings were developed by using attributes derived by aerial photo-interpretation. Input variables of the models were stand mean height, crown closure, site quality, tree species composition, slope, and skidding distance, and average number of trees per cubic metre and average tariff number derived from a stratified field sample plot inventory. Intensive field measurements of 119 mature coniferous stands were used to test the models. Aerial photographs at the approximate scale of 1 : 15 000 were used for photo-interpretation. The bias of the estimated stumpage value was in the range -3.3% to 5.2%. The standard deviation was 2.3-10.8%. For the estimated logging costs the bias was in the range -10.5% to 2.9%. The standard deviation was 4.2-11.7%.},
Keywords = {aerial photographs logging costs photo-interpretation picea abies pinus sylvestris stand inventory stumpage value},
Owner = {hanso},
Timestamp = {2011.11.17}
}

The classification error matrix is frequently used for assessment of the quality of statistical areal estimates provided by remote sensing. The difference between the row and column marginals within individual categories of an error matrix, as the percentage of the total number of subjects, might be a useful measure of systematic differences between the two classifications. The tabulated value of an error matrix is, however, the result of a sampling procedure, and it is important to know whether the differences are significantly larger than expected due to randomness. Formulae for computation of appropriate test statistics are provided, and a numerical example is used to demonstrate the calculation and interpretation of the statistics.

@Article{Naesset1995a,
Title = {A method to test for systematic differences between maps and reality using error matrices},
Author = {Næsset, E.},
Journal = {International Journal of Remote Sensing},
Year = {1995},
Note = {TY - JOUR},
Number = {16},
Pages = {3147-3156},
Volume = {16},
Abstract = {The classification error matrix is frequently used for assessment of the quality of statistical areal estimates provided by remote sensing. The difference between the row and column marginals within individual categories of an error matrix, as the percentage of the total number of subjects, might be a useful measure of systematic differences between the two classifications. The tabulated value of an error matrix is, however, the result of a sampling procedure, and it is important to know whether the differences are significantly larger than expected due to randomness. Formulae for computation of appropriate test statistics are provided, and a numerical example is used to demonstrate the calculation and interpretation of the statistics.},
Keywords = {remotely sensed data classification accuracy agreement},
Owner = {hanso},
Timestamp = {2011.11.17}
}

A material of 1 156 observations was used to develop two Norway spruce (P. abies (L.) Karst.) stand volume functions for western Norway. An additive model initially based on a polynomial of second degree was found to be most suitable. The independent variables were stand basal area, Lorey’s mean height, the product of basal area and mean height, and the square of the mean height. In order to roughly reflect the different climatical conditions within western Norway, all municipalities were classified into the categories ”inland” and ”coast”. The individual observations were assigned the category of the municipality in which they were sited. The type of district was included in the selected additive model by means of a dummy variable approach. Testing showed that two individual regressions should be recommended for prediction purposes in the respective districts. R(2) was 0.998 and C.V. was less than 2.5%. The regressions fitted most parts of the material very well, with exception for low densities for inland districts. Testing by means of an independent data set indicated no systematic differences.

@Article{Naesset1995b,
Title = {Stand volume functions for picea-abies in western norway},
Author = {Næsset, E.},
Journal = {Scandinavian Journal of Forest Research},
Year = {1995},
Note = {TY - JOUR},
Number = {1},
Pages = {42-50},
Volume = {10},
Abstract = {A material of 1 156 observations was used to develop two Norway spruce (P. abies (L.) Karst.) stand volume functions for western Norway. An additive model initially based on a polynomial of second degree was found to be most suitable. The independent variables were stand basal area, Lorey's mean height, the product of basal area and mean height, and the square of the mean height. In order to roughly reflect the different climatical conditions within western Norway, all municipalities were classified into the categories ''inland'' and ''coast''. The individual observations were assigned the category of the municipality in which they were sited. The type of district was included in the selected additive model by means of a dummy variable approach. Testing showed that two individual regressions should be recommended for prediction purposes in the respective districts. R(2) was 0.998 and C.V. was less than 2.5%. The regressions fitted most parts of the material very well, with exception for low densities for inland districts. Testing by means of an independent data set indicated no systematic differences.},
Keywords = {basal area dummy variable mean height picea abies stand volume function},
Owner = {hanso},
Timestamp = {2011.11.17}
}

E. Næsset, “Derivation of a predictive model for production of tree species composition maps at small scales using discriminant function-analysis,” Scandinavian journal of forest research, vol. 10, iss. 1, pp. 90-96, 1995. [Bibtex][Abstract]

Functions for the prediction of tree species composition category were estimated by means of discriminant function analysis. Five tree species composition categories were defined, and the functions were calibrated using the National Forest Inventory sample plots from two counties in eastern Norway. The independent variables were site index, altitude, and slope, which can all be derived from spatial databases. Tree species composition category was predicted for the calibration data and independent test data. The prediction was based on a deterministic and a probabilistic approach. About 30-60% of the observations were correctly classified. The deterministic approach gave large systematic errors for the individual categories. The probabilistic approach gave no such errors. Tree species composition category can be predicted within a GIS environment for production of small scale thematic maps. Although the classification accuracy was rather low, the functions might be used for map production in the absence of better alternatives.

@Article{Naesset1995c,
Title = {Derivation of a predictive model for production of tree species composition maps at small scales using discriminant function-analysis},
Author = {Næsset, E.},
Journal = {Scandinavian Journal of Forest Research},
Year = {1995},
Note = {TY - JOUR},
Number = {1},
Pages = {90-96},
Volume = {10},
Abstract = {Functions for the prediction of tree species composition category were estimated by means of discriminant function analysis. Five tree species composition categories were defined, and the functions were calibrated using the National Forest Inventory sample plots from two counties in eastern Norway. The independent variables were site index, altitude, and slope, which can all be derived from spatial databases. Tree species composition category was predicted for the calibration data and independent test data. The prediction was based on a deterministic and a probabilistic approach. About 30-60% of the observations were correctly classified. The deterministic approach gave large systematic errors for the individual categories. The probabilistic approach gave no such errors. Tree species composition category can be predicted within a GIS environment for production of small scale thematic maps. Although the classification accuracy was rather low, the functions might be used for map production in the absence of better alternatives.},
Keywords = {discriminant function analysis geographical information system thematic maps tree species composition categorical-data},
Owner = {hanso},
Timestamp = {2011.11.17}
}

Mean diameter by basal area (d(g)) is an important stand variable for long-term economic forecasts of forest holdings. In order to use stand-by-stand surveys based on aerial photo interpretation as the data basis for forecasts, d(g) has to be determined. The objective was to develop and test a regression function for d(g) in mature stands of Norway spruce (Picea abies (L.) Karst.) and Scots pine (Pinus sylvestris L.) applicable in southeastern Norway. A study of 700 plots was used to estimate a function for d(g). An additive model was found to be most suitable. The independent variables were potential yield capacity, Lorey’s mean tree height, clown closure determined by ocular estimation by means of aerial photographs, and the product of potential yield capacity and crown closure. The R(2) value was 0.604 and the coefficient of variation was 10.8%. The regression fitted most parts of the calibration data quite well, but it may overestimate the mean diameter in pure spruce stands by 1-2%, and underestimate the diameter in pure pine stands by 3%. For mixed coniferous stands the regression seems satisfactory. Testing by means of an independent data set showed systematic errors of 3-23%. The systematic errors were due partly to calibration problems in connection with the ocular crown closure estimation.

@Article{Naesset1995d,
Title = {Determination of mean diameter by basal area in stands of picea-abies and pinus-sylvestris in southeastern norway by means of aerial photographs},
Author = {Næsset, E.},
Journal = {Scandinavian Journal of Forest Research},
Year = {1995},
Note = {TY - JOUR},
Number = {3},
Pages = {296-304},
Volume = {10},
Abstract = {Mean diameter by basal area (d(g)) is an important stand variable for long-term economic forecasts of forest holdings. In order to use stand-by-stand surveys based on aerial photo interpretation as the data basis for forecasts, d(g) has to be determined. The objective was to develop and test a regression function for d(g) in mature stands of Norway spruce (Picea abies (L.) Karst.) and Scots pine (Pinus sylvestris L.) applicable in southeastern Norway. A study of 700 plots was used to estimate a function for d(g). An additive model was found to be most suitable. The independent variables were potential yield capacity, Lorey's mean tree height, clown closure determined by ocular estimation by means of aerial photographs, and the product of potential yield capacity and crown closure. The R(2) value was 0.604 and the coefficient of variation was 10.8%. The regression fitted most parts of the calibration data quite well, but it may overestimate the mean diameter in pure spruce stands by 1-2%, and underestimate the diameter in pure pine stands by 3%. For mixed coniferous stands the regression seems satisfactory. Testing by means of an independent data set showed systematic errors of 3-23%. The systematic errors were due partly to calibration problems in connection with the ocular crown closure estimation.},
Keywords = {crown closure mean diameter mean tree height norway spruce photo interpretation picea abies pinus sylvestris scots pine},
Owner = {hanso},
Timestamp = {2011.11.17}
}

Data from 16 forest stands were used to assess the accuracy of aerial photo interpretation of areas with thin soil cover and bedrock outcrops. Thin soil cover was defined as soil depth less than 10 cm, including outcrops. The portion of the area with thin soil cover within the stands was interpreted by four experienced persons according to a 4 x 4 Graeco-Latin square design. IR-color photographs and panchromatic black-and-white photographs at the scales 1 : 15 000 and 1 : 22 000 were used. The overall differences between photo interpreted portions and the ground reference portions was not significant. According to an analysis of variance, the interpretation results were slightly better for IR-color film than for the black-and-white film (10% level). The effect of scale was far from significant. The effect of stand density was highly significant. Thin soil cover was underestimated in dense stands. In the future, calibration plots with known characteristics should be used in order to achieve successful photo interpretation of areas with thin soil.

@Article{Naesset1995f,
Title = {Photo interpretation of areas with thin soil cover and bedrock outcrops within forest stands},
Author = {Næsset, E.},
Journal = {Scandinavian Journal of Forest Research},
Year = {1995},
Note = {TY - JOUR},
Number = {1},
Pages = {82-89},
Volume = {10},
Abstract = {Data from 16 forest stands were used to assess the accuracy of aerial photo interpretation of areas with thin soil cover and bedrock outcrops. Thin soil cover was defined as soil depth less than 10 cm, including outcrops. The portion of the area with thin soil cover within the stands was interpreted by four experienced persons according to a 4 x 4 Graeco-Latin square design. IR-color photographs and panchromatic black-and-white photographs at the scales 1 : 15 000 and 1 : 22 000 were used. The overall differences between photo interpreted portions and the ground reference portions was not significant. According to an analysis of variance, the interpretation results were slightly better for IR-color film than for the black-and-white film (10% level). The effect of scale was far from significant. The effect of stand density was highly significant. Thin soil cover was underestimated in dense stands. In the future, calibration plots with known characteristics should be used in order to achieve successful photo interpretation of areas with thin soil.},
Keywords = {bedrock outcrops ir-color photographs nonproductive forest photo interpretation shallow soils},
Owner = {hanso},
Timestamp = {2011.11.17}
}